Sustainable use of washing machine: modeling the...

Institut für Landtechnik

Professur für Haushalts- und Verfahrenstechnik

Prof. Dr. rer. nat. Rainer Stamminger

Sustainable use of washing machine: modeling the consumer

behavior related resources consumption in use of washing

machines

I n a u g u r a l – D i s s e r t a t i o n

zur

Erlangung des Grades

Doktor der Ernährungs- und Haushaltswissenschaft

(Dr. oec. troph.)

der

Landwirtschaftlichen Fakultät

der

Rheinischen Friedrich-Wilhelms-Universität Bonn

vorgelegt am 27.02.2014

von

Dipl. oec. troph. Emir Lasic

aus

Sanski Most, Bosnien und Herzegowina

Referent: Prof. Dr. rer. nat. Rainer Stamminger

Korreferent: Prof. Dr. Michael-Burkhard Piorkowsky

Tag der mündlichen Prüfung: 25. 04. 2014

Erscheinungsjahr: 2014

Abstract

Sustainable use of washing machine: modeling the consumer behavior related

resources consumption in use of washing machines

Two opposing trends are observed in Europe: an increase in the washing machines’

rated capacity and a decrease of the household size (hence a decrease of laundry that

has to be washed). The question poses: what kind of behavior is necessary to use the

washing machines with a higher rated capacity in a more sustainable manner? To

answer this question, a model of a washing machine (virtual washing machine) that is

based on real data of real-life washing machines is constructed. Furthermore, a model

that reproduces, to some extent, the household’s washing behavior (virtual washing

household) is developed.

By conducting parallel simulations of the usage of the virtual washing machine by the

virtual washing households and by varying device-, household- and behavioral

parameters, an optimal parameter combination with the lowest environmental impact

is determined.

The basis for the virtual washing machine is the data gained by testing nine washing

machines of different rated capacity (5 kg, 6 kg, 7 kg, 8 kg and 11 kg). All tests are

conducted in accordance with EN60456:2005, with some modifications regarding the

washing temperatures, load size and detergent dosage.

The predicting power of the virtual washing machine can be considered as good, given

that there are numerous differences in tested washing machines.

The virtual washing household is designed in such a manner that a washing cycle is

conducted when the household has enough laundry collected, so that the rated capacity

of the washing machine can be used. It also offers a possibility to conduct an

“emergency washing cycle” when the time needed for accumulating enough laundry

(to use rated capacity of the washing machine) exceeds the waiting time acceptable by

the consumer (so-called “maximal laundry waiting time”). This model offers a large

range of possibilities to simulate some of the consumer’s behavioral patterns.

With the moderating variable “maximal laundry waiting time”, it is possible to add a

time dimension to the virtual consumer model and thus explore the effects that might

occur when the consumer is ready to postpone an action (in this case the washing of

laundry) to a later point of time. The results show that a sustainable use of washing

machines with a higher rated capacity is possible when consumer behavior changes

towards waiting until enough laundry is accumulated, so that the rated capacity of the

washing machine is used.

Zusammenfassung

Nachhaltige Nutzung einer Waschmaschine: Modellieren des

verhaltensabhängigen Ressourcenverbrauchs bei der Waschmaschinennutzung

Zurzeit sind zwei Trends in Europa zu verzeichnen: einerseits erhöht sich die Anzahl

der Waschmaschinen mit einer höheren Nennfüllmenge und anderseits verringert sich

die Größe der Haushalte und somit der Wäscheanfall. Es stellt sich nun die Frage:

„Wie kann ein Haushalt mit einer kleinen Personenanzahl eine Waschmaschine mit

einer höheren Nennfüllmenge dennoch nachhaltig nutzen?“

Um diese Frage beantworten zu können, wird in dieser Arbeit zunächst, auf Basis von

Daten handelsüblicher Waschmaschinen, ein mathematisches Modell einer

Waschmaschine entwickelt (virtuelle Waschmaschine). Des Weiteren wird ein Modell

eines Haushaltes entwickelt, welches bis zu einem gewissen Grad das Waschverhalten

des Haushaltes nachahmt (virtueller Waschhaushalt).

Mittels paralleler Simulationen der Nutzung der virtuellen Waschmaschinen durch den

virtuellen Waschhaushalt und Veränderung der Geräte-, Haushalts- und

Verhaltensparameter werden geeignete Parameter-Kombinationen gesucht, bei

welchen die Auswirkungen auf die Umwelt am niedrigsten sind.

Die Basis für die virtuelle Waschmaschine bilden Daten aus Waschmaschinentests mit

9 Waschmaschinen verschiedener Nennfüllmengen (5 kg, 6 kg, 7 kg, 8 kg und 11 kg).

Alle Tests werden in Anlehnung an die EN60456:2005 durchgeführt, wobei

Waschtemperatur, Beladungsmenge und Waschmittelmenge modifiziert werden.

Trotz der Vielfalt der Waschmaschinen und den Unterschieden zwischen diesen, kann

die Vorhersagekraft der virtuellen Waschmaschine als gut bewertet werden.

Der virtuelle Waschhaushalt ist so gestaltet, dass ein Waschgang erst dann durchführt

wird, wenn genug Wäsche vorhanden ist, um die Nennfüllmenge der Waschmaschine

komplett auszunutzen. Des Weiteren bietet das Modell die Möglichkeit, einen

sogenannten „Notwaschgang“ durchzuführen. Dieser findet dann statt, wenn die Zeit,

welche nötig ist um genug Wäsche zu sammeln, die vom Verbraucher akzeptierte

Wartezeit (sogenannte „Maximale Wäschewartezeit“) überschreitet.

Dieses Modell ermöglicht die Simulation vieler verbrauchertypischer

Verhaltensmuster.

Mit der moderierenden Variablen „Maximale Wäschewartezeit“ ist es möglich, die

zeitliche Komponente in das Verbraucher-Modell einzubeziehen. Dadurch können

mögliche Effekte aufgezeigt werden, wenn der Verbraucher bereit ist, eine Handlung

(in diesem Fall Wäschewaschen) auf einen späteren Zeitpunkt zu verschieben.

Die Ergebnisse zeigen, dass eine nachhaltige Nutzung der Waschmaschine mit einer

höheren Nennfüllmenge dann möglich ist, wenn der Verbraucher das eigene Verhalten

dahin gehend verändert, dass er wartet, bis sich genug Wäsche angesammelt hat und

so die Nennfüllmenge der Waschmaschine ausgenutzt werden kann.

Content

Abstract

Zusammenfassung

1 Introduction ........................................................................................................... 1

1.1 Washing process ................................................................................................ 1

1.1.1 Mechanics ................................................................................................... 2

1.1.2 Temperature ................................................................................................ 3

1.1.3 Washing time .............................................................................................. 3

1.1.4 Chemistry .................................................................................................... 4

1.2 Washing machines ............................................................................................ 4

1.2.1 Horizontal axis washing machine ............................................................... 5

1.2.2 Vertical axis washing machine ................................................................... 6

1.2.2.1 Impeller (pulsator) type washing machine .......................................... 6

1.2.2.2 Agitator type washing machine ........................................................... 6

1.2.3 Washing machine markets and trends ........................................................ 6

1.2.4 Resource consumption for washing purposes ............................................ 7

1.2.4.1 Electricity consumption ....................................................................... 7

1.2.4.2 Water consumption .............................................................................. 8

1.2.5 Consumer behavior in use of washing machines ....................................... 9

1.2.5.1 Washing frequency ............................................................................ 10

1.2.5.2 Loading of the washing machine ....................................................... 11

1.2.5.3 Use of washing temperature and program ......................................... 12

1.3 Sustainability ................................................................................................... 12

1.4 Demographic trends........................................................................................ 13

1.5 Modeling approaches ...................................................................................... 14

2 Objective .............................................................................................................. 17

3 Material and methods ......................................................................................... 19

3.1 Material ............................................................................................................ 19

3.1.1 Tested washing machines ......................................................................... 19

3.1.2 Washing machine used for standardization and neutralization ................ 19

3.1.3 Test load ................................................................................................... 20

3.1.4 Detergent .................................................................................................. 20

3.1.5 Software .................................................................................................... 21

3.2 Methods ............................................................................................................ 21

3.2.1 Washing machines tests ............................................................................ 21

3.2.1.1 Load ................................................................................................... 21

3.2.1.2 Detergent dosing ................................................................................ 22

3.2.1.3 Temperature/program ........................................................................ 22

3.2.1.4 Normalization and conditioning ........................................................ 23

3.2.1.5 Reproduction tests .............................................................................. 24

3.2.1.6 Evaluation of the soil strips ............................................................... 24

3.2.1.7 Calculation the washing performance index ...................................... 24

3.2.1.8 Testing conditions .............................................................................. 25

3.2.2 Construction of the model of a virtual washing machine ......................... 25

3.2.2.1 Calculations of the consumption in CO2 equivalents ........................ 26

3.2.3 Construction of the model of virtual washing household ......................... 28

3.2.4 Combination of virtual washing household and virtual washing machine30

3.2.4.1 Correction of the detergent amount ................................................... 31

3.2.4.2 Scenario used for simulation ............................................................. 31

3.2.5 Average values calculations ..................................................................... 32

4 Results .................................................................................................................. 33

4.1 Results of the washing machines tests ........................................................... 33

4.1.1 Water consumption ................................................................................... 33

4.1.2 Energy consumption ................................................................................. 36

4.1.3 Washing performance ............................................................................... 37

4.1.4 Duration of the main wash........................................................................ 39

4.1.5 Washing temperature: nominal versus actual washing temperature ........ 40

4.2 Construction of model of virtual washing machine ..................................... 41

4.2.1 Water consumption equation .................................................................... 41

4.2.2 Energy consumption equation .................................................................. 43

4.2.3 Detergent consumption equation .............................................................. 45

4.3 Results of the virtual washing household modeling approach ................... 47

4.4 Results of yearly simulations ......................................................................... 50

4.4.1 Washing machine’s rated capacity versus household size ....................... 50

4.4.2 CO2 equivalent emission of water detergent and energy .......................... 55

4.4.3 Comparison of the time exposure ............................................................. 56

4.4.4 Comparison of average washing temperatures ......................................... 57

5 Discussion ............................................................................................................. 59

5.1 Virtual washing machine ................................................................................ 59

5.1.1 Equation for calculation of water consumption........................................ 59

5.1.2 Total water consumption .......................................................................... 61

5.1.3 Detergent consumption ............................................................................. 64

5.2 Virtual washing household ............................................................................. 66

5.3 Simulations ...................................................................................................... 67

5.3.1 Washing machine’s rated capacity ........................................................... 68

5.3.2 Resource consumption .............................................................................. 69

5.3.3 Washing frequency ................................................................................... 70

5.3.4 Average washing temperature .................................................................. 71

5.4 Deficits of the presented research .................................................................. 73

6 Conclusion ........................................................................................................... 75

7 Future prospects .................................................................................................. 77

8 References ............................................................................................................ 79

9 Abbreviations ...................................................................................................... 85

10 List of figures ....................................................................................................... 87

11 List of tables ......................................................................................................... 91

12 Apendix ................................................................................................................... I

12.1 Washing machine tests data .............................................................................. I

12.2 Virtual washing machine MATLAB source code ........................................ XI

12.3 Virtual washing household MATLAB source code .................................. XIII

Acknowledgements

INTRODUCTION 1

1 Introduction

Laundry washing, in the sense of cleaning textiles in aqueous liquor, is a complex

process involving the cooperative interaction of numerous physical and chemical

influences. In the broadest sense, washing can be defined as both removal by water or

by an aqueous detergent solution of poorly soluble residues, as well as the dissolution

of water-soluble impurities. (JAKOBI and LÖHR, 1987, SMULDERS et al., 2007)

TERPSTRA defines the primary objective of cleaning as “…restoration of the fitness for

use and the esthetical properties of the textiles, e.g. removal of soil, stains, odors and

creases and regaining surface smoothness and thermal isolation.”

(TERPSTRA, 2001, p.4) LEMARE defines other benefits from laundering processes, such

as the maintenance of the appropriate hygiene (LEMARE, 1987).

1.1 Washing process

The washing process is described as a function of different: washing temperatures,

length of washing cycles, types and amounts of detergent and applied mechanical

works. It is best described by using a circle, which many researchers today refer to as

Sinner’s Circle (Figure 1-1). (SINNER, 1960) STAMMINGER adds a further fifth parameter,

water (inner circle), which represents the combining element of all factors

(STAMMINGER, 2013).

Figure 1-1: Sinner’s circle (own representation)

Temperature

Chemistry Time

Water

Mechanics

INTRODUCTION 2

Each of these four factors can be substituted to a certain degree by the other three

factors and the resulting washing performance remains the same. For example, a

decrease of the temperature can be partially compensated by increasing either one or

more of the other factors, i.e. chemistry, washing time or mechanical agitation.

(WAGNER, 2011, KUTSCH et al., 1997, SMULDERS, 2007)

The combination of different factors depends on the washing technique employed. In

the case of washing laundry by hand, the portion of the mechanics is much higher than

the portion of the washing time (Figure 1-2). Laundry washing in a washing machine

at a higher temperature results in a higher contribution of the temperature in the

washing process (Figure 1-3).

Figure 1-2: Sinner's circle for washing by hand (own representation)

Figure 1-3: Sinner's circle for washing in a washing machine at a high temperature (own

representation)

1.1.1 Mechanics

In the washing process, the textiles are mixed with the wash liquor. The total amount

of wash liquor required in the main wash process is made up of two portions: liquor

soaked by the laundry (bound wash liquor) and liquor that remains free in the drum

(free wash liquor). (SMULDERS et al., 2007) In the case of manual laundering, the

mechanical action is done by rubbing and beating, stretching and squeezing of the

textiles. Sometimes this is done by using auxiliary devices. (SMULDERS et al., 2007)

In washing machines, the mechanical work is determined by: diameter of the drum,

reverse rhythm, water level, number of revolutions of the drum, position and number

Chem.

Temp. Mech.

Time Water

Chem.

Temp.

Mech.

Time

Water

INTRODUCTION 3

of the paddles in the drum, the amount of load, and the duration of the wash cycle.

(HLOCH et al., 1989)

The extent of the mechanical action imparted to the laundry can be altered by changing

the ratio of dry laundry weight to the volume of wash liquor (laundry-liquor-ratio). A

low wash liquor level and relatively rapid reversal causes a large mechanical effect on

the laundry. In contrast, a high wash liquor and slower reversal provides less

mechanical action. (SMULDERS et al., 2007)

1.1.2 Temperature

With an increase of the temperature, the soil removal normally increases. The elevated

temperature enhances the chemical reactions, solubilizes greasy soils and weakens the

binding forces of the soil on the fabric. (SMULDERS et al., 2007)

The maximal washing temperature is determined by the properties of the wash load.

With an increase of the temperature, the soil-binding capacity of the wash liquor

decreases, so that an extensive soil redisposition can be anticipated. Furthermore, with

an increase of the temperature, the knitting of the synthetic fibers increases as well.

In order to prevent those opposing trends in the praxis, the laundry washing process is

divided into different sub-processes. Today, normally the laundry washing is divided

into prewash and main wash, which is followed by the rinsing cycle. (HLOCH et al.,

1989)

1.1.3 Washing time

In order to remove the dirt from the textiles, the washing factors of chemistry and

mechanics have to interact together with the load at a certain washing temperature for

a certain period of time (WAGNER, 2011).

The duration of the washing process determines how long the detergent is allowed to

act. Longer cleaning times will increase the soil removal and thus improve the

cleaning performance (VAUGHN et al., 1941).

INTRODUCTION 4

At the beginning of the washing process, it is necessary to heat up the cold inlet water.

Heat-up time is the time required to achieve the preset temperature. Heat-up time

depends on the amount of laundry and hence the water quantity, the temperature of the

inlet water and the heating capacity of the washing machine. This heat-up time is

followed by the main washing time. (KUTSCH et al., 1997, SMULDERS, 2007, WAGNER,

2011)

1.1.4 Chemistry

The factor chemistry is accompanied with the medium water. A quantifying of this

factor in the form of a single operand is not possible since the washing chemistry does

not only depend on the very complex coaction of different detergent components, but

also depends on other factors, such as temperature or soil composition. (HLOCH et al.,

1989)

Besides the actual removal of soil, further functions of the chemical substances are:

water softening, emulation of lipid component, dispersion and stabilization of

particulate soil, bleaching of stains and dissolving of proteins. (KUTSCH et al., 1997)

The washing performance normally increases as the detergent concentration increases.

When the detergent concentration is too high, the washing performance decreases,

because a high foam production leads to a decrease of the mechanical force.

(HLOCH et al., 1989)

1.2 Washing machines

Washing machines that are presently used globally in domestic laundry care can be

divided mainly into two categories depending on the orientation of their axis:

horizontal and vertical axis washing machines. Following are the different types of

those automatic-washing systems. (KUTSCH et al., 1997)

INTRODUCTION 5

1.2.1 Horizontal axis washing machine

This type of washing machine consists of a stainless steel, perforated drum into which

the laundry is loaded, and an outer surrounding tub into which the water is filled. The

mechanical agitation of the laundry is done by rotating the drum around a horizontal

axis. Paddles, which are mounted on its inside, lift the laundry to the top and then let it

tumble down. (WAGNER, 2011; KUTSCH et al., 1997; SMULDERS, 2007)

Because the wash action does not require the clothing to be fully suspended in water,

only enough water is needed in order to moisten the fabric and to ensure a sufficient

transfer of suds between the water-soaked laundry and the free water. An electric

heater element on the bottom of the drum allows the heat-up of the water in the main

wash phase to the preset washing temperature, mainly between 30 °C and 60 °C.

(WAGNER, 2011; KUTSCH et al., 1997; SMULDERS, 2007)

Today’s state of the art domestic washing machines have many features built in, such

as a large number of different programs for all kind of textiles, automatic detection of

amount and type of textile, automatic dosing, steam generator for laundry refreshing

purposes, etc. (WAGNER, 2011; KUTSCH et al., 1997; SMULDERS, 2007)

Furthermore, modern washing machines frequently use the so-called fuzzy logic

control which governs partial processes of the washing program. Such a control is able

to automatically provide, for example, variable speeds of reversion of the drum

depending on the amount of foam produced during the wash cycle, or to control the

quantity of water intake and washing time depending on weight and type of wash load.

All those features should help the consumer to treat the laundry optimally.

(SMULDERS et al., 2007)

The horizontal axis type of washing machine is mainly used in Europe and Near East

countries. In recent years, this type of machine has also been gaining market shares in

all other regions as well. (WAGNER, 2011; KUTSCH et al., 1997; SMULDERS, 2007)

INTRODUCTION 6

1.2.2 Vertical axis washing machine

This type of washing machine uses a vertically mounted perforated drum that is itself

contained within a watertight tub. The laundry is freely suspended in the water. The

mechanical work is performed by moving the laundry in the water. Depending on its

laundry motion configuration, these washing machine can be subcategorized into:

1.2.2.1 Impeller (pulsator) type washing machine

In this type of machine, an impeller located on the bottom of the washing machine

induces a vortex. The vortex causes the laundry to circulate up and down in the water.

This type of washing machine is common in Japan, China and South Korea. These

machines normally do not contain a heating element, but use cold (or otherwise

preheated) water from the tap. (WAGNER, 2011; KUTSCH et al., 1997; SMULDERS, 2007)

1.2.2.2 Agitator type washing machine

In this type of washing machine, the mechanical work is produced by a device

(agitator) located in the center of the drum, which then rotates around its vertical axis

to the right and left. These machines normally are connected to an external hot and

cold water supply and mix these waters accordingly. This type of washing machine is

common in the USA and Canada. (WAGNER, 2011; KUTSCH et al., 1997; SMULDERS,

2007)

1.2.3 Washing machine markets and trends

The EU-27 washing machine stock in the residential sector was estimated to be around

172,85 million units (Bertoldi and Atanasiu, 2007). According to data presented in the

Preparatory Study for Eco-design Requirements of Energy-using Products (EuP) the

penetration of the washing machines on the European market is high: Germany

(96 %), Czech Republic (94,9 %), Poland (86 %), 87, France (94,7 %), Hungary

INTRODUCTION 7

(88 %), Spain (98,5 %), UK (94 %) Finland (87 %), Sweden (72 %).

(PRESUTTO et al, 2007)

The share of washing machines with a higher rated capacity has grown in the past

years. On the European market, the average washing machine load capacity has

increased from 4,8 kg in 1997 up to 5,4 kg in 2005 (CECED, 2005) and is still

increasing. In 2010, washing machines with a rated capacity between 5,5 kg and 7 kg

were the most important market segment in 10 EU countries (AT, BE, DE, ES, FR,

GB, IT, NL, PT, SE) (BERTOLDI et al., 2012). In 2012, the top selling appliances were

those with a 7 kg rated capacity (Henkel, private communication).

Figure 1-4: Average load capacity trends of household washing machines

(Data source: GfK cited from BERTOLDI et al., 2012, own representation)

1.2.4 Resource consumption for washing purposes

1.2.4.1 Electricity consumption

The residential sector accounts for 29,71 % of total electricity used, the second largest

electricity consumption sector. Within the residential sector, 7,2 % of electricity is

consumed for laundry care. This shows the importance of washing and drying in the

total electricity consumption. (BERTOLDI et al., 2012)

INTRODUCTION 8

The Eco-design preparatory study estimated the energy consumption of the washing

machine stock in 2005 to be around 51 TWh / year, with an average yearly

consumption per appliance of 295 kWh in the EU-27 households. (PRESUTTO et al,

2007)

A very comprehensive study regarding the energy consumption for washing purposes

worldwide is delivered by PAKULA AND STAMMINGER, 2010. With their study, they

covered roughly 780 million washing machines in about 2.3 billion households, which

is about one third of the world population. PAKULA AND STAMMINGER note that most

countries outside of their investigation still do their laundry washing by hand.

According to their calculated estimation, the global electricity consumption of washing

machines may be, at maximum, about 100 TWh /year of electricity. (PAKULA and

STAMMINGER, 2010)

1.2.4.2 Water consumption

Total water abstraction in the European Union (EU 27) amounts to about 247 000

million m³/year. On average, 44 % of total water abstraction in European Union is

used for energy production, 24 % for agriculture, 17 % for public water supply and

15 % for industry (DWORAK et al., 2007).

The Organization for Economic Co-operation and Development estimates that the

share of the household in the total water consumption amounts to ca. 10 % to 30 %

(OECD, 2013). Average consumption in OECD countries is about 100 000 l/year and

person equals 174 liters per person per day. According to the European Environmental

Agency, the average water consumption in Europe is between 100 liters and 320 liters,

with an average of 155 liters per person and day (EEA, 2005).

Pakula and Stamminger estimate that 20 000 000 000 m3 water is consumed worldwide

for laundry washing in a washing machine (PAKULA and STAMMINGER, 2010).

INTRODUCTION 9

1.2.5 Consumer behavior in use of washing machines

When taking into account the cradle-to-grave life cycle of a washing machine, i.e.

including the production, use and disposal phase, it becomes obvious that the use

phase is the most resource-consuming and waste-producing (Figure 1-5).

Figure 1-5: Life cycle analysis of a washing machine (Source: RÜDENAUER et al.)

Since washing machines are operated on consumer demand, their consumption of

water, energy and detergents for a washing cycle is determined mainly by

consumer-driven factors such as the frequency of washing, loading behavior, selection

of the washing temperature/program and dosing of detergents (STAMMINGER et al.,

2008).

Much research regarding the behavior of the consumer when washing laundry has

been conducted in the past years. However, much of this research was conducted by

producers of the detergent and appliance industry, and hence was partially published

or not published at all.

The most relevant studies of the European consumer washing behavior that have been

published in the past 10 years are listed below.

0

50

100

Energy

CO2

CSB

Waste

INTRODUCTION 10

2003 ARILD et al. conducted a survey with 4010 participants from Greece, the

Netherlands, Norway and Spain with the goal of finding out how the consumers handle

the washing of laundry (ARILD et al., 2003).

JÄRVI and PALOVIITA conducted interviews in 2004 and 2005 with 340, in this case 299,

people in Finland with the goal of finding out how consumers

read/understand/implement the information and instructions on detergent (JÄRVI AND

PALOVIITA, 2007).

In 2007, the University of Bonn conducted an online survey as part of a Preparatory

Study for Eco-design Requirements of Energy-using Products. A total of 2500

participants from 10 European countries took part in the survey (PRESUTTO et al, 2007).

In 2007 BERKHOLZ et al. conducted a non-representative study on behalf of the German

Federal Ministry of Economics and Technology and studied the washing behavior of

100 German households over a period of one month. The participants of the study

were actually measuring the amount of laundry, detergent and electricity consumed for

washing purposes. (BERKHOLZ et al., 2007)

STAMMINGER and GOERDELER published results of a survey conducted in 2005 with

3750 participants from all parts of Germany regarding their behavior in laundry

washing. (STAMMINGER and GOERDELER, 2007)

In 2008 and 2011, the international association for soaps, care and cleaning products

(AISE) conducted a survey with 5060 consumers in 23 European countries with an

aim to find out laundry washing behavior. (AISE, 2008; AISE, 2011)

Not all studies have had the same research question; therefore the results of different

studies cannot always be compared. However, the following data regarding the

washing frequency, loading behavior and washing temperature selection is available.

1.2.5.1 Washing frequency

The number of washing cycles depends on factors such as the washing machine’s rated

capacity or household size, loading behavior, etc. In the literature, information

INTRODUCTION 11

regarding the consumers’ behavior when using washing machines is sometimes

contradictory.

According to RÜDENAUER and GRIEßHAMMER, 164 wash cycles per year for an average

household is conducted in Germany, Austria, and Switzerland. RÜDENAUER and

GRIEßHAMMER list that the average number of washing cycles starts with 111 wash

cycles for a one-person household and increases to 211 wash cycles per year for a

four-person household (RÜDENAUER and GRIEßHAMMER, 2004)

STAMMINGER and GOERDELER published 4,5 washes per week (234 per year) as an

average household number of washing cycles in Germany. Those figures are based on

online questionnaires of more than 2000 persons. (STAMMINGER and GOERDELER, 2007)

PRESUTTO et al, state that the average washing frequency is 2,6 times per week for

single- and 6,2 for four-person households. On average, 4,9 wash cycles per household

per week, which equals on average 254 per year, are conducted. (PRESUTTO et al, 2007)

1.2.5.2 Loading of the washing machine

Many studies show that consumers do not fully use the rated capacity of their washing

machine. BERKHOLZ et al. measured that, for example in Germany, the average load of

a washing machine is 3,2 kg per wash cycle (BERKHOLZ et al., 2007).

According to PRESUTTO et al., most consumers claim to use the full loading capacity of

their washing machine, but this does not mean that the rated capacity is really used

(PRESUTTO et al, 2007).

DE ALMEIDA et al. state that the vast majority of the households always use the washing

machine at over 75 % of its capacity. (DE ALMEIDA et al., 2006)

Another measurement study shows that for an average washing machine capacity of 5

kg, consumers consider an average load of 3,7 kg to be a full load

(KRUSCHWITZ et al., 2014).

INTRODUCTION 12

1.2.5.3 Use of washing temperature and program

According to PRESUTTO et al., the average nominal washing temperature in Europe is

45,8 °C. The most used program is the 40 °C program (37 %), the second most used

temperature is 60 °C, which is 23 % of all the washes (PRESUTTO et al., 2007).

AISE publication also states that the 40 °C program is the most used program with

40 % of all the washes (AISE, 2013).

According to STAMMINGER and GOERDELER, the average washing temperature in

Germany is 46,3 °C. The most used washing temperature is 40 °C with 37 % of all

wash cycles. The second and third most used washing temperatures are 60 °C with 30

% of all washing cycles resp. 30 °C with 27 % of all washing cycles. (STAMMINGER and

GOERDELER, 2007)

1.3 Sustainability

For weighing the climatic impact of emission of different greenhouse gases, the

Intergovernmental Panel on Climate Change (IPCC) has, since its first assessment in

1990, used the Global Warming Potential (GWP). (CHANGE, 1990)

The GWP has been subjected to much criticism because of its formulation, but

nevertheless it has retained some favor because of the simplicity of its design and

application, and also its transparency compared to proposed alternatives.

(SHINE et al., 2005)

In December 1997, the Kyoto Protocol was adopted (BREIDENICH et al., 1998). It

entered into force on 16 February 2005 and for the first time it establishes legally

binding limits for 37 industrialized countries on emissions of carbon dioxide and other

greenhouse gases. (KyotoProtocol, 2013)

Kyoto Protocol set a binding reduction target for EU-15 GHG emissions of 8 % on

average for the 2008–2012 period, compared with 1990 levels. In February 2011, the

European Council reconfirmed the EU objective of reducing greenhouse gas (GHG)

emissions by 80–95 % by 2050, as compared to levels in 1990 (COMMISSION, 2011).

INTRODUCTION 13

Figure 1-6: Contribution of the different sectors to the GHG emission (Source: EEA, 2010)

The contribution of the household and services to the global GHG emission is 14,5%

(EEA, 2010). The EU objective of reducing greenhouse gas emissions can only be

achieved when all the sectors contributing to the GHG emission contribute to that goal.

By lowering the energy consumption, a total GHG emission can be achieved.

1.4 Demographic trends

According to the convergence scenario of EUROPOP2010, the EU-27’s population is

projected to increase to 525 million by 2035, peaking at 526 million around 2040, and

thereafter gradually declining to 517 million by 2060 (EUROSTAT, 2013) .

Not only is an increase of the population expected, but the number of households with

a fewer number of household members is also increasing.

According to a study published by OECD by 2025-2030, one-person households will

make up around 40 % or more of all households in the following European countries:

Austria, France, Germany, the Netherlands, Norway, Switzerland and England. The

authors of the study see the reason for this largely as a consequence of an ageing

Energy production

31,1%

Manufact. / construction

12,4% Transport 19,6%

Households / services

14,5%

Fugitive emissions

1,7%

Industrial processes

8,3%

Agriculture 9,6%

Waste 2,8%

INTRODUCTION 14

population, increase of the sole-parent household and current fertility rates and

increases in life expectancy. (OECD, 2011)

1.5 Modeling approaches

There are different approaches to model the washing machines and their processes.

Since the modeling of the washing machine is mainly done in the industry, the

published research in this field is scarce. However, a selection of different modeling

approaches is presented.

Parametrical modeling was used by WARD. In this research, the trajectory of a single

concentrated mass was parametrically modeled in order to estimate the pressure drops

that would occur on the mass as it is lifted by the baffles of the rotating washing

machine drum and then subsequently dropped and impacted upon landing. (WARD,

2000)

TERPSTRA conducted research with an aim to evaluate whether the cleaning

performance of domestic washing machines can be assessed with test soils by using

the statistical modeling of the real experimentally-generated data. (TERPSTRA, 2001)

In 2003, PARK and WASSGREN conducted a computational simulation of textile

dynamics inside a rotating drum. They used a simplified discrete element

computational model to model the movement of the textiles. The textiles were

modeled as spherical bundles, and their movement and interactions were modeled on

the basis of the macroscopic behavior of these spherical textile bundles. (PARK and

WASSGREN, 2003)

LAZAREVIĆ and VASIĆ used a mathematical modeling approach to model washing

machines, where it is seen as a conglomerate of rigid multi-body systems. The basic

properties of the washing machine are the basis for the construction of the model.

(LAZAREVIĆ and VASIĆ, 2008)

RAMASUBRAMANIAN and TIRUTHANI used computational modeling to develop firstly a

simplified 2D model, and then later a 3D model of a washing machine. The goal of the

research was, by using the computer model, to develop a better understanding of the

INTRODUCTION 15

dynamics of modern washing machines that use balance rings. (RAMASUBRAMANIAN

and TIRUTHANI, 2009)

MAC NAMARA et al. used the Positron Emission Particle Tracking (PEPT) technique,

where radioactively labeled particles are monitored. In the research, a single tracer

particle attached to a textile was monitored as it rotated and tumbled amongst other

textiles in a commercially available domestic washing machine. The aim of the

research was to understand the mechanisms by which mechanical action is imparted

onto wet textiles during washing. (MAC NAMARA et al., 2012)

IN 2005, RÜDENAUER et al. conducted a life cycle assessment for washing machines to

model the complex interaction between a product and the environment. The model

includes the life cycle of a product from the production, use and disposal phases. In the

study, RÜDENAUER et al. compare the acquisition and use of a washing machine with a

larger rated capacity to the acquisition and use of a washing machine with a rated

capacity of 5 kg under environmental and economic aspects. (RÜDENAUER et al., 2005)

The presented modeling approaches focus on a specific aspect of a washing machine

(e.g. exploring the influence of mechanics in a washing process), but none of the

models depicts the whole washing process. Furthermore, those models also do not

include the washing machine’s rated capacity, except that of RÜDENAUER et al. Finally,

the role of the consumer / household is either only rudimentary or not included in

those models at all.

INTRODUCTION 16

OBJECTIVE 17

2 Objective

On the European market at present, two opposing trends can be observed. On one

hand, an increase of the average load size of the washing machines that are sold on the

market is observed. On the other hand, the demographic structure of the European

households is changing, resulting in a decrease of the average household size and

hence results in a reduced amount of laundry to be washed.

In addition to those two opposing trends, it should be kept in mind that consumers at

present do not fully use the rated capacity of their washing machines, and consider a

partial load level as a full load.

Those considerations lead to the question: “What kind of washing behavior is needed,

so that the usage of washing machines with a higher rated capacity has a low impact

on the environment?”

In order to answer that question, the following needs to be done:

Firstly, a model of a virtual washing machine that is based on average and

typical data of washing machines available on the market shall be developed.

The virtual washing machine should be flexible, so that the input parameters

selected by the consumer can be varied.

Secondly, a virtual washing household that incorporates various washing

behavioral parameters shall be developed.

Thirdly, a simulation of the usage of the virtual washing machine by the virtual

washing households shall be developed. By conducting parallel simulations and

by varying device-, household-, and behavioral parameters, optimal parameter

combinations with low environmental impact shall be determined and hence

conclusions regarding an optimal consumer behavior are to be drawn.

OBJECTIVE 18

MATERIAL AND METHODS 19

3 Material and methods

All material and methods used for testing the washing machines are based on the

experimental setup required in EN60456:2005.

The framework of EN60456:2005 is broadened and tests with a load of 25 % and 75 %

as well as tests with detergent under- and overdose are included. Those cases are

marked accordingly. In some cases, the EN60456:2010 is applied and also those cases

are marked accordingly.

3.1 Material

3.1.1 Tested washing machines

All tested washing machines are front loading washing machines and are prepared in

accordance with the manufacturer's specified safety instructions and installed in

accordance with EN60456:2005.

Table 3-1: List of tested washing machines

Manufacturer and washing machine model rated capacity Ident. Nr:

Miele Novotronic W1514 5 kg WM1 Indesit IWB 5125 5 kg WM2

BOSCH Maxx6 Eco Wash WAE2834P 6 kg WM3

Bauknecht WA UNIQ 714FLD 7 kg WM4

AEG Öko Lavamat 76850 A 7 kg WM5

BOSCH Logixx8 Vario Perfekt WAS32792 8 kg WM6

Haier HW-F1481 8 kg WM7

Indesit PWE 8168W 8 kg WM8

Bauknecht WAB-1210 11 kg WM9

3.1.2 Washing machine used for standardization and neutralization

MIELE W1514 NOVOTRONIC and WASCATOR CLS are used for the normalization process

of the test load. MIELE NOVOTRONIC W1514 program software is modified so that it

complies with the standard procedure as described in EN60456:2005.


Table 3-2: Equipment used for the normalization process of the load

Washing machine washing machines tested

W1514 Novotronic WM1, WM3, WM4, WM5, WM6 Wascator WM2, WM7, WM8, WM9

3.1.3 Test load

The test load consists of the base load and the soil strips as specified. Base load is used

in accordance with EN60456:2005 and consists of cotton bed sheets, cotton

pillowcases, and cotton towels. Soiled test trips are in accordance with EN60456:2010

and were manufactured by “EMPA Testmaterialien”.

Table 3-3: Soil strips and respective batch number used for the tests

EMPA Batch number Washing machines tested

Batch 21 WM1, WM3, WM4, WM6 Batch 27 WM5 Batch 35 WM2, WM7, WM8 Batch 45 WM9

3.1.4 Detergent

Reference detergent A* is used in the test as specified by the EN50456:2005. During

the testing time, all three components are stored separately and used within the expiry

date as stated in the documentation provided by the manufacturer.

Table 3-4: Detergent

Component batch Nr.

Washing machines tested base powder sodium

perborate tetrahydrate

bleach activator (TAED)

WM1, WM3, WM5, WM6 167-513 237-394 24704603

WM2, WM4, WM7, WM8, WM9 237-970 287-984 26746203


3.1.5 Software

Table 3-5 lists all software used.

Table 3-5: Overview of software used

Software name Used for

Matlab 2010a Simulation of a washing process IBM SPSS Statistics 21 Multivariate statistics i.e. Multiple linear regression Colorimeter software Evaluation of the EMPA soil strips Alborn Data logger software used to monitor and record the test

experiment

3.2 Methods

3.2.1 Washing machines tests

The framework of EN60456:2005 is extended so that some test parameters such as

temperature, detergent dosage and load size are varied.

In addition, differing from EN60456:2005 in which five repetitions of each parameter

setting are required, in this case one repetition is conducted for each parameter setting.

3.2.1.1 Load

The load is varied so that tests with 25 %, 50 %, 75 % and 100 % of the rated washing

machine load capacity were conducted. Furthermore, tests with no load were

performed in order to simulate a very small load. Table 3-6 shows the loading

scenarios for different washing machines.

Table 3-6: Loading scenarios for different washing machines’ rated capacities

WM rated capacity

Load size 25 % in kg




5 kg 1,25 2,5 3,75 5 6 kg 1,5 3 4,5 6 7 kg 1,75 3,5 5,25 7 8 kg 2 4 6 8 11 kg 2,75 5,5 8,25 11


3.2.1.2 Detergent dosing

In order to reflect a more consumer-relevant behavior in the testing procedure, the

amount of the nominal dose of detergent was calculated as specified in

EN60456:2010.

Nominal dosing:

To simulate the over- and under-dosing, the following dosages are included.

Overdosing:

Under dosing:

Prior to the dosing the detergent, the detergent dispenser is cleaned and dried.

3.2.1.3 Temperature/program

The washing temperature is varied so that the washing program at 30 °C, 40 °C, and

60 °C are conducted. Combining all varied parameters results in a total of 39 tests

conducted for one single washing machine. Washing program is “cotton” and the spin

speed is 1400 rpm. During the washing cycle the water temperature in the sump is

monitored by a sensor placed in the space between the inner and outer drum at the

bottom of the washing machine’s drum.

Table 3-7: Test design and variation of the parameters

Load size

Temperature Number of

30 °C 40 °C 60 °C tests

0 % No detergent No detergent No detergent 3

25 % Over dose

Nominal dose Under dose

Over dose Nominal dose Under dose


9

50 % Over dose




9

75 % Over dose

Nominal dose Under-dose



9

100 % Over dose




9


The experimental setup is based on the following recurring test scheme based on

EN60456:2005 (Figure 3-1).

Figure 3-1: Example of a testing procedure on an example of a 25 % load

3.2.1.4 Normalization and conditioning

In accordance with the EN60456:2005, the normalization process is conducted in

batches of 3,75 kg. If the quantity exceeded the quantity of 3,75 kg, the laundry

amount is divided into smaller load sizes.

Conditioning is conducted as described in EN60456:2005. The individual items of

laundry are left for 24 hours on a rack. All items are separately hung on, so that a

circulation of the air is ensured.

Normalizing and conditioning

Detergent dosage 50 %

Cotton 30 °C Cotton 40 °C Cotton 60 °C





Normalizing

Normalizing


3.2.1.5 Reproduction tests

Every constellation of the parameters is tested once. In order to test the reproducibility

of the results, every washing machine is tested in so-called reproduction tests. For this

reason, three tests in accordance with EN60456:2005 with a full load, using the

washing program "cotton 60 °C", and with a detergent dosage of 100 % are conducted.

3.2.1.6 Evaluation of the soil strips

Upon completion of a test, the test load is removed no later than ten minutes after the

completion of the washing process. The attached soil strips are separated. Test loads

without the soil strips is then weighted and dried. The separated soil strips are air-

dried, ironed, and evaluated by measuring the tristimulus Y reflectance in accordance

with EN60456:2005.

3.2.1.7 Calculation the washing performance index

According to the standard EN60456:2005, the CIE Y values calculated for every

washing machines test is set in proportion to those of a reference washing machine.

In EN60456:2005, the reference washing machines tests are conducted by washing

5 kg of laundry in a reference washing machine and using 180 g of the reference

detergent.

In EN60456:2010, the reference washing machine tests are conducted the same as in

EN60456:2005, except that the detergent dosage is 110 g.

In this research, the reference washing machine tests are not conducted. Instead, the

documentation provided by the soil strip producer “Empa Testmaterialien” is used.

The values in the documentation are provided only in accordance with EN60456:2005

for 180 g and 135 g. Based on those values, the CIE Y values for 110 g are

extrapolated.


Table 3-8: CIE Y values of different batches and extrapolated values for 110g of detergent

Batch Number

CIE Y values for 180 g

CIE Y values for 135 g

Extrapolated Y CIE values for 110g

21 355,1 345,7 340,5 27 353,6 342,9 337,0 35 353,5 340,7 333,6 45 348,7 336,1 329,1

3.2.1.8 Testing conditions

All testing conditions are maintained as defined in EN60456:2005

Table 3-9: Testing conditions in accordance with EN60456:2005

Parameter Interval

Ambient temperature 23 ± 2 °C Water temperature 15 ± 2 °C Water pressure 2,4 ± 0,5 bar Water hardness 2,5 ± 0,2 mmol / L Power supply voltage 230 V ± 1 % Power supply frequency 50 Hz ± 1 %

3.2.2 Construction of the model of a virtual washing machine

During the washing machine tests, input parameters (independent variables) are:

washing temperature, load size, washing machine’s rated capacity, duration of the

main wash and amount of detergent. The output parameters (dependent variables) are:

Energy consumption, water consumption, washing performance and washing time.

By using this data, a model of a virtual washing machine is constructed. This model

consists of a set of multiple linear regression equations that puts into relation the input

and output parameters, and hence can be used to predict the amount of resources

consumed during a single washing cycle (detergent, water and energy).


The basis for the construction of the virtual washing machine model is a general

equation for multiple linear regressions.

𝑌 𝑋 𝑋 (3-1)

Where is:

𝑌 𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒

𝑋 𝑋 𝑋 𝑖𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠

𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠 𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑒𝑑 𝑤𝑖𝑡𝑕 𝑖𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒

Based on the eq. (3-1) and the input parameters, three different multiple regression

equations are developed. Each of the equations is used to predict one of the consumed

resources (i.e. amount of the detergent, water and energy consumed for a single

washing cycle).

3.2.2.1 Calculations of the consumption in CO2 equivalents

In order to be able to compare different resources (water, detergent and energy), its

respective amounts are converted into the CO2 equivalents by using the conversion

values as presented in Table 3-10.

Table 3-10: Overview of CO2 conversion factors

Factor Gram CO2 per unit Unit

CO2WATER1 6,16 liter

CO2ENERGY2 601 kWh

CO2DETERGENT3 1,7 gram

For the calculation of CO2 equivalents for detergent consumed during a single washing

cycle ( ), the following equation is used:

1 Source: Jungbluth, N., des Gas, S. V. & Wasserfaches, S. (2006) Vergleich der Umweltbelastungen von

2 Source: Icha, P. (2013) Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in

den Jahren 1990 bis 2012. Climate Change, Umweltbundesamt, 07/2013.

3 Source: Henkel, private communication


𝐶𝑂𝐸 CO DETERGENT (3-2)

Where is:

𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑑𝑒𝑡𝑒𝑟𝑔𝑒𝑛𝑡 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑎 𝑠𝑖𝑛𝑔𝑙𝑒 𝑤𝑎𝑠𝑕𝑖𝑛𝑔 𝑐𝑦𝑐𝑙𝑒

For the calculation of CO2 equivalents for water consumed during a single washing


𝐶𝑂𝐸 CO WATER (3-3)

Where is:

𝑋 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑑𝑒𝑡𝑒𝑟𝑔𝑒𝑛𝑡 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑎 𝑠𝑖𝑛𝑔𝑙𝑒 𝑤𝑎𝑠𝑕𝑖𝑛𝑔 𝑐𝑦𝑐𝑙𝑒

For the calculation of CO2 equivalents for energy consumed during a single washing


𝐶𝑂𝐸 CO ENERGY (3-4)

Where is:

For the calculation of total CO2 equivalents consumed during a single washing cycle

( ), the following equation is used:

𝐶𝑂𝐸 𝐶𝑂𝐸 𝐶𝑂𝐸 𝐶𝑂𝐸 (3-5)

The following figure shows a schematic of the model construction


DetergentWater

consumption

Energy

consumption

Input and output parameter

CO2 conversion

CO2 usage for one washing cycle

CO2

conversion

CO2

conversion

Figure 3-2: Schematic of the mathematical model construction of a washing machine (own representation)

3.2.3 Construction of the model of virtual washing household

In order to simulate the usage of the virtual washing machine, the following concept of

virtual washing household is presented.

It is assumed that the usage of the washing machine is defined by:

1. Washing machine related factors (washing machine’s rated capacity and

duration of the washing programs)

2. Household related factors, such as the household size, amount and type of

laundry that has to be washed per person and week.

3. Washing behavior related factors (e.g. washing temperature, loading behavior

and dosing behavior )

Simulation of a routine structure as an example for a washing of laundry that has to be

washed in a 30 °C program is presented in Figure 3-3. Analogously, the simulation

structure can be applied to a 40 °C and 60 °C washing program.


Percentage of 30°C

laundry in percent

Number

of

persons

Washing cycle 30°C

Remains

of laundryIf LA30 ≥ LF30

YES

Washing machine load

size in kg: (WMLS)

Loading behavior for

30°C (LB30)

Counter

of

laundry

remains

If counter of laundry

remains

= MLWT

Maximal

laundry waiting

time in weeks

(MLWT)

NO

YES

Set counter of laundry

remains to zero

Laundry amount 30°C

(LA30)NO

SET LA30

TO:

LA30 – LF30

Weekly additional

laundry per person in kg

Load factor 30 (LF30)=

WMLS*LB30

Figure 3-3: Example of a schematic of the simulation

Simplified, the simulation can be described by the following five steps:

1) Parameter induction

a. Setting of the washing machine’s rated capacity

b. Setting of the number of persons in household

c. Setting the amount of laundry that has to be washed per person and per

week.

d. Setting of the maximal laundry waiting time (MLWT)

e. Set the week counter to zero

2) Calculation prior to wash

a. Determine the amount of rest load from previous week. In the first week

this value is zero.

b. Calculation of the washing cycle load amount by multiplying the

washing machine’s rated capacity with the loading factor.

c. Calculation of the available amount of laundry that has to be washed

3) Washing process simulation


a. If the available amount of laundry that has to be washed is lower than

washing cycle load amount, continue to (4).

b. If the available amount of laundry that has to be washed is equal to or

higher than washing cycle load amount, conduct the washing simulation.

Repeat this step until the available amount of laundry is lower than the

washing cycle load amount. The remaining amount of laundry is added

to the next week’s amount of laundry.

4) Add one week to the week counter.

5) Check whether the duration of the simulation has been reached – if not, restart

from Step (2).

6) If yes , end of the simulation.

With every week, the new amount of laundry per person is introduced into the

simulation.

3.2.4 Combination of virtual washing household and virtual washing machine

Simulation of long-term usage is conducted by combining the model of the virtual

washing household and the model of the virtual washing machine.

By using Matlab software, a program that combines those two segments is

programmed so that the following input parameters can be varied:

- Washing machine’s rated capacity

- Number of persons in household

- Amount of laundry per person

- Type of laundry per person

- Maximal laundry waiting time

- Loading behavior for certain types of laundry

- Duration of the simulation


3.2.4.1 Correction of the detergent amount

It is to be expected that in some cases, when a small load is washed in a washing

machine with a high rated capacity, the calculated amount of detergent is very low.

However, consumers always dose a certain amount of detergent. In order to include

this consumer behavior in the model, the following convention is implemented in the

yearly simulation:

When the amount of calculated detergent for a single wash is lower than 40 g

(EN60456:2010 base amount of detergent), the calculated amount of detergent is

automatically corrected to 40 g.

3.2.4.2 Scenario used for simulation

The simulations are conducted by using the following general simulations parameters:

Duration of the simulation = 52 weeks

Weekly additional laundry = 4 kg per person

The average composition of the load (KRUSCHWITZ et al., 2014)

Washing load at 30 °C = 23 % of total load



The maximal loading factor for:

30 °C washing cycle = 50 % of the rated capacity




3.2.5 Average values calculations

For the calculation of the average values, the following formula was used:

(3-6)

Where is:

�̅� 𝑎𝑟𝑖𝑡𝑕𝑚𝑒𝑡𝑖𝑐 𝑚𝑒𝑎𝑛 𝑥 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑣𝑎𝑙𝑢𝑒𝑠 𝑛 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠

RESULTS 33

4 Results

In this chapter, all data received by the experiments, mathematical modeling, and

simulations are worked out and presented.

4.1 Results of the washing machines tests

An overview of the data of the washing machines tests is presented.

4.1.1 Water consumption

Figure 4-1: Water consumption versus load of nine washing machines - washing temperature is 60 °C

Water consumption increases with an increase of the load size for all washing

machines tested (Figure 4-1). Comparison of slopes indicates that washing machines

differ in their ability to adapt its water consumption to the amount of load. The slope

of the WM3 shows that the load size has the lowest influence on water consumption.

Other washing machines have quantity controls that are more sensitive to a variation

of the load.

0 25 50 75 1000

20

40

60

80

100

Load in percent

Wa

ter

co

nsu

mp

tio

n in

lite

r

WM1 (5 kg)

WM2 (5 kg)

WM3 (6 kg)

WM4 (7 kg)

WM5 (7 kg)

WM6 (8 kg)

WM7 (8 kg)

WM8 (8 kg)

WM9 (11 kg)

RESULTS 34

Figure 4-2: Comparison between the specific water consumption and the load size

With an increase of the load, specific water consumption decreases. Variations in

specific water consumption are lowest when the washing machine is fully loaded, and

highest when the washing machine is loaded with 25 % of the rated capacity. When

washing machines are underloaded, the differences in specific water consumption for

washing nearly same amount of load vary to up to twice (Figure 4-2).

Figure 4-3: Water consumption versus load amount - washing temperature is 60 °C

0 2 4 6 8 100

5

10

15

20

25

30

Load in kilogram

Sp

ecific

Wa

ter

co

nsu

mp

tio

n in

l/k

g

WM1 (5 kg)

WM2 (5 kg)

WM3 (6 kg)

WM4 (7 kg)

WM5 (7 kg)

WM6 (8 kg)

WM7 (8 kg)

WM8 (8 kg)

WM9 (11 kg)

0 25 50 75 1000

20

40

60

80

100

120

Load in percent

Wa

ter

co

nsu

mp

tio

n d

ecre

ase

in

pe

rce

nt

WM1 (5 kg)

WM2 (5 kg)

WM3 (6 kg)

WM4 (7 kg)

WM5 (7 kg)

WM6 (8 kg)

WM7 (8 kg)

WM8 (8 kg)

WM9 (11 kg)

RESULTS 35

Setting the water consumption of a fully-loaded washing machine as 100 % reveals

that water reduction is not proportional to the load reduction (Figure 4 3).

A load decrease of 50 % leads, in the best case, to a water consumption decrease of

less than 40 % (in the worst case, the decrease is less than 5 %).

Table 4-1: Ratio between the main wash water consumption and the total water consumption (mean values), in dependence of the detergent dosage

Detergent dose in percent of a nominal dose

50 % 100 % 150 % Mean

WM1 1:3,4 1:3,4 1:3,3 1:3,4 WM2 1:3,3 1:3,3 1:3,3 1:3,3 WM3 1:4,0 1:3,9 1:3,7 1:3,9 WM4 1:4,0 1:4,0 1:4,0 1:4,0 WM5 1:3,5 1:3,5 1:3,6 1:3,5 WM6 1:4,1 1:4,1 1:4,1 1:4,1 WM7 1:3,4 1:3,3 1:3,3 1:3,3 WM8 1:3,2 1:3,1 1:3,2 1:3,2 WM9 1:3,3 1:3,3 1:3,3 1:3,3

Mean

1:3,5

Table 4-2: Ratio between the main wash water consumption and the total water consumption (mean values), in dependence of the load size

Washing machine load in percent of rated capacity

25 % 50 % 75 % 100 % Mean

WM1 1:3,0 1:3,4 1:3,5 1:3,6 1:3,4 WM2 1:3,8 1:3,4 1:3,1 1:2,9 1:3,3 WM3 1:4,8 1:4,0 1:3,4 1:3,3 1:3,9 WM4 1:3,8 1:3,8 1:3,8 1:4,5 1:4,0 WM5 1:3,5 1:3,9 1:3,4 1:3,3 1:3,5 WM6 1:4,3 1:4,2 1:3,9 1:3,9 1:4,1 WM7 1:3,6 1:3,5 1:3,2 1:3,0 1:3,3 WM8 1:3,5 1:3,3 1:3,0 1:2,9 1:3,2 WM9 1:3,4 1:3,3 1:3,2 1:3,2 1:3,3

Mean

1:3,5

The ratio values, in dependence of the load size, show the lowest mean values in the

case of the washing machine WM8 (1:3,2) and the highest values in the case of the

WM6. The mean ratio is 1:3,5.

RESULTS 36

4.1.2 Energy consumption

Figure 4-4: Energy consumption in kWh versus load size. Washing temperature 60 °C

Similar to the water consumption, the energy consumption increases with an increase

of the load size. WM4 is an exception and consumes at the load level of 25 % more

than when it is fully loaded (Figure 4-4).

Figure 4-5: Energy consumption versus load amount - washing temperature is 60 °C

0 2 4 6 8 100

0.5

1

1.5

2

Load in kilogram

Ene

rgy c

on

sum

ption

in k

Wh

WM1 (5 kg)

WM2 (5 kg)

WM3 (6 kg)

WM4 (7 kg)

WM5 (7 kg)

WM6 (8 kg)

WM7 (8 kg)

WM8 (8 kg)

WM9 (11 kg)

0 25 50 75 1000

20

40

60

80

100

120

Load in percent

Ene

rgy c

on

sum

ption

decre

ase in p

erc

en

t

WM1 (5 kg)

WM2 (5 kg)

WM3 (6 kg)

WM4 (7 kg)

WM5 (7 kg)

WM6 (8 kg)

WM7 (8 kg)

WM8 (8 kg)

WM9 (11 kg)

RESULTS 37

Setting the energy consumption of a fully loaded washing machine as 100 % reveals

that energy reduction is not proportional to the load reduction. A decrease of the load

by 50 % results in a decrease of energy consumption of maximally 20 %.

Furthermore, WM6 and WM9 consume 20 % more energy when underloaded (75 % or

the rated capacity) than when fully loaded. WM4 consumes 10 % more energy when

underloaded (25 % of the rated capacity) than when fully loaded.

4.1.3 Washing performance

Figure 4-6: Index of washing performance versus load for cotton 60 °C

With an increase of the load size, the washing performance decreases. WM3 shows the

lowest washing performance index values for all loading sizes (Figure 4-6).

25 50 75 1000.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Load in percent

Inde

x o

f w

ashin

g p

erf

orm

an

ce

WM1 (5 kg)

WM2 (5 kg)

WM3 (6 kg)

WM4 (7 kg)

WM5 (7 kg)

WM6 (8 kg)

WM7 (8 kg)

WM8 (8 kg)

WM9 (11 kg)

RESULTS 38

Figure 4-7: Index of washing performance vs. energy consumption. From left to right the energy values indicate the machines' energy use for 30 °C, 40 °C and 60 °C washing

programs

With an increase of the washing temperature, the energy consumption and the washing

performance increases. In the case of WM1, an increase of the washing temperature

from 30 °C to 40 °C results in an increase of the energy consumption of 0.13 kWh and

the washing performance increases 0,003 index points.

In the case of the WM8, an increase of the washing temperature from 30 °C to 40 °C

results in an increase of the energy consumption of 0,63 kWh and a washing

performance increase of 0,207 index points. An increase of the washing temperature

from 40 °C to 60 °C results in an increase of the energy consumption of 0,24 kWh and

an increase of the washing performance of 0,018 washing performance index points.

(Figure 4-7).

0 0.5 1 1.50.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Energy consumption in kWh

Ind

ex o

f w

ash

ing

pe

rfo

rma

nce

WM1 (5 kg)

WM2 (5 kg)

WM3 (6 kg)

WM4 (7 kg)

WM5 (7 kg)

WM6 (8 kg)

WM7 (8 kg)

WM8 (8 kg)

WM9 (11 kg)

RESULTS 39

Figure 4-8: Index of washing performance vs. detergent dosage

Washing performance varies between different washing machines. An adjustment of

the washing performance can also be done by varying the amount of the detergent.

Comparison of the slopes shows a slightly higher loss in performance when the dosage

is reduced from 100 % to 50 % than from 150 % to 100 % (Figure 4-8).

Table 4-3: Overview of average washing performance achieved at different washing temperatures

Washing temperature

Average washing performance at specific temperature

Washing performance setting in the simulation

30 °C 0,9288

0,93 40 °C 0,9984

1,00

60 °C 1,0449

1,04

4.1.4 Duration of the main wash

Comparison of average main washing duration data shows a large variety among

different washing machines types and nominal washing temperatures. With an increase

of the washing temperature, the duration of the main wash increases as well in most

cases. The lowest values are reached by the washing machine WM3 at 30 °C (18

minutes) and the highest values are reached by WM9 at 60 °C (173 minutes).

50 100 1500.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Detergent dosage in percent

Ind

ex o

f w

ash

ing

pe

rfo

rma

nce

WM1 (5 kg)

WM2 (5 kg)

WM3 (6 kg)

WM4 (7 kg)

WM5 (7 kg)

WM6 (8 kg)

WM7 (8 kg)

WM8 (8 kg)

WM9 (11 kg)

RESULTS 40

Table 4-4: Overview of different average durations of the main wash at the following washing temperatures

Duration of the main wash cycle in minutes at the

following temperatures (average values)

30 °C 40 °C 60 °C

WM1 56 56 55 WM2 39 82 82 WM3 18 25 37 WM4 65 72 83 WM5 65 66 81 WM6 74 86 94 WM7 61 61 72 WM8 31 160 156 WM9 156 158 173

4.1.5 Washing temperature: nominal versus actual washing temperature

The washing temperature set on the washing machine (nominal washing temperature)

and the washing temperature measured by the washing machines sensor (actual

washing temperature) do not always match. In the case of the nominal temperature of

30 °C, the average actual temperature values range between 26 °C and 35 °C. In the

case of a nominal temperature of 40 °C, the average actual temperature values range

between 39 °C and 48 °C, and in the case of nominal temperature of 60 °C, the

average actual temperatures range between 49 °C and 63 °C (Table 4-5).

Table 4-5: Overview of average actual washing temperatures that were achieved instead of the respective nominal temperature

Average actual washing temperatures in °C

30 °C 40 °C 60 °C

WM1 29 38 56 WM2 35 48 59 WM3 33 44 63 WM4 29 40 56 WM5 30 39 55 WM6 26 41 56 WM7 27 37 57 WM8 29 43 51 WM9 32 39 49

RESULTS 41

4.2 Construction of model of virtual washing machine

The virtual washing machine model consists of a set of equations, each used to

calculate one of the resources consumed during a washing cycle (water, energy and

detergent). Figure 4-9 shows a schematic (based on Figure 3-3) on how the virtual

washing machine is constructed.

Water

consumption

Energy

consumption

CO2 usage for one washing cycle

Load,

washing

machine’s

rated capacity

Temperature,

main wash

duration

CO2

conversion

CO2

conversion

Detergent

Washing performance,

temperature, load, main

wash duration

CO2 conversion

Figure 4-9: Schematic of a construction of a virtual washing machine based on the data received during the washing machine tests

4.2.1 Water consumption equation

Multiple linear regression analysis (Table 4-6) was used to develop a model for

predicting water consumption from load size and rated capacity. The two-predictor

model is able to account for 88 % of the variance in water consumption.

RESULTS 42

Table 4-6: Summary statistics, correlations, and results from the regression analysis (water consumption)

multiple regression weights

Variable mean std correlation with

WC b β

Water consumption (WC) 14,772 5,1606

Load size (LS) 4,2 2,571 0,929*** 1,725*** 0,860

WM Rated capacity (WMRC) 7,2 1,77 0,527*** 0,476*** 0,162

Constant 4,116

R = 0,940

R2 = 0,884

R2Adjusted = 0,883

F = 1359,228***

* p < 0,05 ** p < 0,01 ***p<0,001

Tests to see if the data met the assumption of collinearity indicated that

multicollinearity is not a concern: Load size (Tolerance = 0,820, VIF = 1,220), rated

capacity (Tolerance = 0,820, VIF = 1,220).

The histogram of standardized residuals indicated that the data contained

approximately normally distributed errors, as did the normal P-P plot of standardized

residuals, which showed points that were not completely on the line, but close. The

scatterplot of standardized residuals showed that the data met the assumptions of

homogeneity of variance and linearity.

The general regression equation (3-1), in combination with the variables selected by

the multiple regression analysis, is used to construct a model. The final equation for

predicting the water consumption during the main wash is:

𝑪𝑾 𝟒 𝟏𝟏𝟔 𝟏 𝟕𝟐𝟓 𝐋𝐎𝐀𝐃 𝟎 𝟒𝟕𝟔 𝑾𝑴𝑽 (4-1)

Where is:

LOAD 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑙𝑎𝑢𝑛𝑑𝑟𝑦 𝑖𝑛 𝑘𝑔

𝑊𝑀𝑉 𝑟𝑎𝑡𝑒𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑖𝑛 𝑘𝑔 𝐶𝑊𝑚𝑤 𝑤𝑎𝑡𝑒𝑟 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡𝑕𝑒 𝑚𝑎𝑖𝑛 𝑤𝑎𝑠𝑕

RESULTS 43

The ratio between the water consumed during the main wash to total water

consumption is 1:3,5 (Table 4-1 and Table 4-2), so that the equation for calculation of

the total amount of water consumed for a single washing cycle ( ) is the

following:

𝑪𝑾 𝑪𝑾 𝟑 𝟓 (4-2)

Combining the eq. (4-1) and the eq. (4-2) equation (4-3) results:

𝑪𝑾 𝟑 𝟓 (𝟒 𝟏𝟏𝟔 𝟏 𝟕𝟐𝟓 𝐋𝐎𝐀𝐃 𝟎 𝟒𝟕𝟔 𝑾𝑴𝑽) (4-3)

4.2.2 Energy consumption equation

Analogous to the construction of the equation for calculating the water consumption,

the equation for calculating energy consumption is constructed. Multiple linear

regression analysis is used (Table 4-7) to develop a model for predicting energy

consumption based on water consumption, washing temperature and the duration of

the main wash. The three-predictor model is able to account for 92 % of the variance

in energy consumption, F (3, 351) = 1323,465; p < 0,001.

Table 4-7: Summary statistics, correlations and results from the regression analysis (energy consumtion)


Variable mean std correlation with WC

b β

Energy consumption (EC) 0,525 0,3157

Water consumption (WC) 14,74 5,021 0,470*** 0,02246*** 0,357 Washing temperature (WT) 41,90 11,115 0,743*** 0,02040*** 0,720

Duration of main wash (DMW) 78,95 42,994 0,642*** 0,00247*** 0,337

Constant -0,836

R 0,959

R2 0,919

R2Adjusted 0,918

* p < 0,05 ** p < 0,01 ***p<0,001

RESULTS 44

Table 4-8: Correlations between predictor variables

EC WC WT DMW

Energy consumption (EC) Water consumption (WC) 0,470***

Washing temperature (WT) 0,743*** -0,09*

Duration of main wash (DMW) 0,642*** 0,527*** 0,162***

* p < 0,05 ** p < 0,01 ***p<0,001


multicollinearity is not a concern: Water consumption in the main wash (Tolerance =

0,690, VIF = 1,449), washing temperature (Tolerance = 0,931, VIF = 1,074) duration

of the main wash (Tolerance = 0,678, VIF = 1,476).



residuals, which showed points that were not completely on the line, but close. The

scatterplot of standardized residuals showed that the data met the assumptions of

homogeneity of variance and linearity.

Based on the results of the multiple regression analysis, the final equation to predict

the energy consumption is constructed. The general regression equation (3-1), in

combination with the variables selected by the multiple regression analysis, is used to

construct a model. The final equation for predicting the energy consumption during the

main wash is:

𝑪 𝟎 𝟖𝟓𝟔 𝟎 𝟎𝟐 𝑴 𝟎 𝟎𝟐𝟒 𝑪𝑾 𝟎 𝟎𝟎𝟐𝟓 𝑴𝑾 (4-4)

Where is:

𝐶𝐸 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑒𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡𝑕𝑒 𝑚𝑎𝑖𝑛 𝑤𝑎𝑠𝑕 𝑖𝑛 𝑘𝑊𝑕 𝑇𝐸𝑀𝑃 𝑎𝑐𝑡𝑢𝑎𝑙 𝑤𝑎𝑠𝑕𝑖𝑛𝑔 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝐶𝑊 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑 𝑏𝑦 𝑢𝑠𝑖𝑛𝑔 𝑒𝑞 10 𝑀𝑊𝐷 𝑚𝑎𝑖𝑛 𝑤𝑎𝑠𝑕 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑚𝑖𝑛𝑢𝑡𝑒𝑠

RESULTS 45

The energy consumed during the spin phase, as well the energy consumed in

standby / left on/off mode, is not included in the total energy consumption.

4.2.3 Detergent consumption equation

Analogous to the water and energy consumption prediction equation, the multiple

linear regression analysis is used to develop a model for predicting the washing

performance index from independent variables: load size, detergent, actual washing

temperature and the duration of the main wash.

Basic descriptive statistics and regression coefficients are shown in Table 4-9. The

four-predictor model is able to account for 82 % of the variance in the washing

performance index.

Table 4-9: Summary statistics, correlations and results from the regression analysis (detergent consumption)


Variable mean std correlation with

WP b β

Washing performance (WP) 0,992 0,095

Load size 4,5 2,39 -0,255*** -0,034584*** -0,868 Detergent 93,9 48,96 0,270*** 0,001194*** 0,618 Actual washing temperature 42 11,16 0,467*** 0,002691*** 0,311 Duration of main wash 81 43,71 0,452*** 0,001365*** 0,652 Constant = 0,812372

R = 0,907

R2 = 0,823

R2

Adjusted = 0,821 F= 302,661*** * p < 0,05 ** p < 0,01 ***p<0,001

Table 4-10: Collinearity statistics

Constant Tolerance VIF

Load size

0,552 1,811 Detergent

0,662 1,512

Actual washing temperature 0,942 1,062 Duration of the main wash 0,752 1,330

RESULTS 46


multicollinearity is not a concern.



residuals, which showed points that were not completely on the line, but close.

The scatterplot of standardized residuals showed that the data met the assumptions of

homogeneity of variance and linearity. Based on the results of the multiple regression

analysis, the final equation to predict the amount of detergent is constructed.

The general regression equation (3-1), together with the variables selected by the

multiple linear regression analysis, is used to construct a model. The result is equation

(4-5).

𝑾 𝟎 𝟖𝟏𝟐𝟒 𝟎 𝟎𝟎𝟐𝟔 𝟎 𝟎𝟑𝟒𝟔 𝐋𝐎𝐀𝐃 𝟎 𝟎𝟎𝟏𝟐 𝐃 𝟎 𝟎𝟎𝟏𝟒 𝐃

(4-5)

As the equation is intended to be used to calculate the amount of detergent consumed

during a washing cycle, it is solved for detergent. The result is equation (4-6).

𝑾 𝟎 𝟖𝟏𝟐𝟒 𝟎 𝟎𝟎𝟐𝟔 𝑴 𝟎 𝟎𝟑𝟒𝟔 𝐋𝐎𝐀𝐃 𝟎 𝟎𝟎𝟏𝟒 𝐃

𝟎 𝟎𝟎𝟏𝟐 (4-6)

Where is:

WP p x TEMP p °C LOAD k DET MWD

RESULTS 47

4.3 Results of the virtual washing household modeling approach

The results of programming of the virtual washing household are presented by

showing the course of the laundry stock. All examples feature the laundry stock of 15

weeks. In all figures the lines are for visualization purposes only. For the ease of

understanding the following examples, the scenario as defined in 3.2.4.2 does not

apply and following simplified scenario is assumed.

Each week: 1 kg of 30 °C laundry is added to the stock

2 kg of 40 °C laundry is added to the stock

3 kg of 60 °C laundry is added to the stock

Loading factor: 30 °C washing cycle = 50 % of the rated capacity



Figure 4-10: Example of 30 °C laundry stock course in a 5 kg rated capacity machine when 1 kg of laundry is added to the stock every week

The results of dynamic simulation of a virtual washing household show the course of

the laundry stock of a household that uses a washing machine with the rated capacity

of 5 kg. Every week 1 kg of laundry is added to the stock. The washing cycle is

conducted when the laundry stock is 5 kg (Figure 4-10).

Week 0 5 10 15

0

2

4

6

8

10

Am

ount

of la

undry

in

kg

Washing cycles at 30°C

RESULTS 48

Figure 4-11: Laundry stock course with three different washing programs/temperatures in a 5 kg rated capacity washing machine when every week 1 kg, 2 kg and 3 kg of laundry are

added to the 30 °C, 40 °C and 60 °C laundry stock

In addition to the course of 30 °C laundry stock, the courses of 40 °C and 60 °C

laundry stock are presented. In the case of the 40 °C laundry stock course, every week

2 kg of laundry is added, and in the case of the 60 °C laundry stock course 3 kg of

laundry is added to the stock (Figure 4-11).

Figure 4-12: Impact of the loading factor (implemented in the case of 30 °C load) on the laundry stock course of three different washing programs/temperatures in a 5 kg rated

capacity washing machine when every week 1 kg, 2 kg and 3 kg of laundry are added to the 30 °C, 40 °C and 60 °C laundry stock.

0 5 10 150

2

4

6

8

10

Week

Am

ou

nt of

laundry

in k

g




0 5 10 150

2

4

6

8

10

Week

Am

ou

nt of

laundry

in k

g




RESULTS 49

In the case of 30 °C, the load factor of 0.5 is implemented, so that the washing

machine conducts a washing cycle when the laundry stock reaches at least 50 % of the

washing machine’s rated capacity. In the third week, the 30 °C laundry stock reaches 3

kg of which 2,5 kg are washed so that 0,5 kg were left. In fourth week, 1 kg of laundry

is added so that the total amount of laundry is 1,5 kg (Figure 4-12).

Figure 4-13: Impact of the maximal laundry waiting time (implemented in all three programs/temperatures) on the laundry stock course of three different washing

programs/temperatures in a 5 kg rated capacity washing machine when every week 1 kg, 2 kg and 3 kg of laundry are added to the 30 °C, 40 °C and 30 °C laundry stock.The impact is

visible in the reduction of the laundry stock in the third week.

Implementation of the maximal laundry waiting time in the simulation routine is

visible in the course of all three washing temperatures. The maximal laundry time is

set to “up-to-2-weeks”. In the second week, all three laundry stocks are reduced

(Figure 4-13).

0 5 10 150

2

4

6

8

10

Week

Am

ou

nt of

laundry

in k

g




RESULTS 50

4.4 Results of yearly simulations

4.4.1 Washing machine’s rated capacity versus household size

The results of the simulation of a yearly usage of the virtual washing machines with

the washing machine’s rated capacity (WMRC) of 5 kg, 8 kg, and 11 kg by the virtual

washing household are presented. Each data point in the chart shows how much CO2

equivalent is emitted when the washing machine is used by a household (of different

sizes) for one year. The lines are for visualization purposes only.

The following maximal laundry waiting time (MLWT) abbreviations are applied:

MLWT = 0 maximal laundry waiting time is “up to 1 week”

MLWT = 1 maximal laundry waiting time is “up to 2 weeks”



Figure 4-14: Comparison of emission of CO2 equivalents when MLWT = 0

0 1 2 3 4 5 6 70

50

100

150

200

Number of person in household

Em

issio

n o

f C

O2 e

quiv

ale

nts

in

kg

Rated capacity - 5 kg



RESULTS 51

When the maximal laundry waiting time is “up to 1 week”, the lowest CO2 equivalents

emission values are in the case of 1-person household:

Washing machine with 5 kg WMRC emits 51,4 kg of CO2 equivalents


Washing machine with 11 kg WMRC emits 82,5 kg of CO2

The highest values are in the case of 7-person household:




Slopes of washing machines with 8 kg and 11 kg rated capacity interchange in the case

of a 4-person household. The slopes of washing machines with 5 kg and 8 kg rated

capacity interchange between the household sizes of 2- and 3-persons.

Figure 4-15: Comparison of emission of CO2 equivalents when MLWT =1

When the maximal laundry waiting time is “up to 2 weeks”, the lowest values are in

the case of 1-person household:



0 1 2 3 4 5 6 70

50

100

150

200


Em

issio

n o

f C

O2 e

quiv

ale

nts

in

kg




RESULTS 52


The highest values are in the case of 7-person household




Slopes interchange at 2-person household tick mark.

Figure 4-16: Comparison of emission of CO2 equivalents when MLWT =2

When the maximal laundry waiting time is “up to 3 weeks”, the slope of the washing

machine with a rated capacity of 8 kg and 5 kg have the lowest CO2 equivalents

emission values for a 1-person household with 27 kg (WMRC = 8 kg) and 27,6 kg

(WMRC = 5 kg). The highest CO2 equivalents emission is in the case of the washing

machine with a 5 kg rated capacity (189,7 kg). The interchange of the slope course is

at the 1-person household tick mark.

0 1 2 3 4 5 6 70

50

100

150

200


Em

issio

n o

f C

O2 e

quiv

ale

nts

in k

g




RESULTS 53

Figure 4-17: Comparison of emission of CO2 equivalents when MLWT = 3

When the maximal laundry waiting time is “up to 4 weeks”, the lowest values of CO2

emission are in the case of the washing machine with a rated capacity of 8 kg and a

1-person household with 24,8 kg of CO2 equivalents emitted. The highest values of

CO2 equivalents emission are in the case of the washing machine with a rated load

capacity of 5 kg with 189,7 kg in the case of a 7-person household.

0 1 2 3 4 5 6 70

50

100

150

200


Em

issio

n o

f C

O2 e

quiv

ale

nts

in k

gRated capacity - 5 kg



0 2 4 6 0

50

100

150

200

Em

issio

n o

f C

O2

equ

iva

len

ts in

kg

5 kg

0 2 4 6 0

50

100

150

200 8 kg

0 2 4 6 0

50

100

150

200

Number of persons in household

11 kg

RESULTS 54

Figure 4-18: Overview of washing machine’s rated capacity vs. MLWT

An overview of all washing machines in dependence of the maximal laundry waiting

time show a strong gap between the slopes of the curves as the loading capacity of the

washing machine increases.

RESULTS 55

4.4.2 CO2 equivalent emission of water detergent and energy

Figure 4-19: CO2 equivalents emission split by individual resource consumption share

where MLWT = 0 (4-19a) and MLWT = 3 (4-19b). Each of the seven columns within each rating capacity class represents household ranging from 1-person households (First column)

to 7-person households (Seventh column).

CO2 equivalents emission is split by the individual share of the resources consumed.

The share of the energy and detergent in the total CO2 equivalent emission are the

highest. The share of the CO2 emission for water is minimal.

5kg 8kg 11kg0

50

100

150

200

Washing machine rated capacity

Em

issio

n o

f C

O2 e

quiv

ale

nts

5kg 8kg 11kg0

50

100

150

200


Em

issio

n o

f C

O2 e

quiv

ale

nts

a b

RESULTS 56

4.4.3 Comparison of the time exposure4

Figure 4-20: Time exposure in dependence of the rated capacity, household size and MLWT (Starting with maximal laundry waiting time of “up to 1 week” (Chart a) to maximal laundry

waiting time of “up to 4 weeks” (Chart d)).

Time exposure depends on the maximal laundry waiting time, washing machine’s

rated capacity and the number of persons in the household. Figures a-d show the time

exposure in dependence of the maximal laundry waiting time. The time exposure is the

highest when the maximal laundry waiting time is the lowest. With an increase of the

4 Time consumed for washing (expressed in number of washing cycles). It is composed of the regular washing

cycles (cycles where the rated capacity of the washing machine is used) and emergency washing cycles (cycles

where the washing machine’s rated capacity is not used due to reaching the maximal laundry waiting time).

1 2 3 4 5 6 70

100

200

300

400

Number of Person in household

Num

be

r of w

ashin

g c

ycle

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Regular washing cycles

Emergency washing cycles

1 2 3 4 5 6 70

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Number of Person in householdN

um

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1 2 3 4 5 6 70

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Num

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ashin

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1 2 3 4 5 6 70

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Num

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ycle

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a b

c d

RESULTS 57

maximal laundry waiting time, the total time exposure as well as the share of the

emergency washing cycles decreases.

4.4.4 Comparison of average washing temperatures5

Figure 4-21: Comparison of average washing temperatures in dependence of the household size: 1-person household (4-21a) – 4-person household (4-21d)

5 Arithmetically-averaged washing temperature of all washing cycles conducted during one year

5kg 8kg 11kg30

35

40

45


Ave

rage te

mp

era

ture

in °

C

1 Person (MLWT = 0)

1 Person (MLWT = 1)

1 Person (MLWT = 2)

1 Person (MLWT = 3)

5kg 8kg 11kg30

35

40

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Ave

rage te

mp

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ture

in °

C

2 Person (MLWT = 0)

2 Person (MLWT = 1)

2 Person (MLWT = 2)

2 Person (MLWT = 3)

5kg 8kg 11kg30

35

40

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Ave

rage te

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ture

in °

C

3 Person (MLWT = 0)

3 Person (MLWT = 1)

3 Person (MLWT = 2)

3 Person (MLWT = 3)

5kg 8kg 11kg30

35

40

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Ave

rage te

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era

ture

in °

C

4 Person (MLWT = 0)

4 Person (MLWT = 1)

4 Person (MLWT = 2)

4 Person (MLWT = 3)

a b

c d

RESULTS 58

With an increase of the maximal laundry waiting time, the average washing

temperature decreases. The values are the lowest when the maximal laundry waiting

time is “up to 4 weeks” (40 °C e.g. 2-person household). The highest values are in the

case when the maximal laundry waiting time is “up to 1 week” (43 °C in e.g. 1-person

household).

DISCUSSION 59

5 Discussion

5.1 Virtual washing machine

The first goal of this study was to develop a model of a virtual washing machine that is

based on the data of washing machines available on the market. Although three

equations are necessary to describe the washing process, it can be concluded that the

goal of a construction of a virtual washing machine is reached.

The fact that three equations are necessary to describe the washing machine process is

due to its complexity. On one hand, there are numerous variables that impact the

process, and on the other hand there are three dependent variables where each has to

be calculated by using a separate predicting model. In the following, the individual

equations are discussed.

5.1.1 Equation for calculation of water consumption

The function of water in the washing process is manifold. It is not only the medium

that transports the detergent and heat to the fabric surface and facilitates the removal of

the soil, but it is also the medium where the soil is stabilized so that a redeposition is

prevented. (JAKOBI and LÖHR, 1987)

Since 1970, numerous inventions have led to a decrease of the water consumption.

Average water consumption of 200 liters in 1970 (STAMMINGER et al., 2005) decreased

to today’s consumption of 42,6 liters (VHK, 2013).

The data of the washing machine’s tests (Figure 4-1 and Figure 4-2) and the resulting

equation show that load size and rated capacity are the two most influential factors on

the water consumption of a washing machine.

However, data also reveals that there are many variations among washing machines in

their ability to adapt the water consumption to the load, especially when they are not

fully loaded. In some cases, the specific water consumption for washing nearly same

DISCUSSION 60

amount of load can vary up to double the amount when the washing machine is

underloaded (Figure 4-2).

As the washing machines test data shows, the differences among washing machines’

abilities to adapt the water consumption to the load is lowest when the washing

machine is fully loaded. This is confirmed also by (RÜDENAUER et al., 2004), who also

found out that with a decrease of the load size, the specific consumption of the water

and energy increases.

The consumers, however, do not fully load their washing machine. They consider, for

example, a load of 3,7 kg to be a full load in a washing machine which has a rated

capacity of 5 kg (KRUSCHWITZ et al., 2014). In order to lower the resource

consumption, most of today’s washing machines are equipped with sensors that

control the washing machine and automatically adapt the resource consumption to the

load. With a decrease of the load size, the water consumption decreases.

(WAGNER, 2011)

As data shows, this is not always the case.

The fact that fully loaded washing machines show the lowest variance in the water

consumption might be viewed as a direct consequence of the energy label standard

EN60456:2005, which was in force when the tested washing machines were produced.

The standard stipulated that washing machines are to be tested with a full load when

tested according to the energy label. There was no incentive for the manufacturers of

white goods to optimize the washing machines to anything other than full-load size.

The reason for variations in the water consumption when the washing machine is not

fully loaded, however, might be because of the accuracy of the sensor and fuzzy logic

implemented. It is possible that the sensors do not react precisely, and therefore

influence the fuzzy inference so that variations in water consumption occur.

The data shows that the washing machine’s rated capacity influences the water

consumption as well. Washing machines with a higher rated capacity have higher

absolute water consumption; however, with an increase of the load size, the specific

DISCUSSION 61

water consumption decreases. A possible reason for this is that washing machines let

in some amount of water, whether it is loaded or not. This minimal amount of water is

needed for the protection of the washing machine namely some of its components,

such as the heater, which might suffer damage when preheating and running without

water that would absorb the heat. In washing machines with a higher rated capacity,

this minimal amount has a lower impact on the specific water consumption than in the

case of washing machines with a lower rated capacity.

5.1.2 Total water consumption

Total water consumption depends mainly on the number of rinsing cycles (WAGNER,

2011). The number of the rinsing cycles again depends on what the manufacturer

wants to offer to its consumers as a standard program.

In some cases, the consumers are offered a washing program with a lower number of

rinsing cycles as a standard, and the consumers have to use additional options such as

“water plus” or “extra rinsing” in order to receive a higher rinsing performance.

Alternatively, some manufacturers offer the consumers a washing program with a

higher water consumption, i.e. with more rinsing cycles as a standard washing

program and it is up to the consumers to include the water-saving options, such as

“economy wash” or “half load” in order to save water.

In some cases, even the sensors that monitor the washing process can influence the

water consumption. For example, the number of rinsing cycles can vary depending on

the quality of the foam detection sensors.

A look into the comparison of the average values of total water consumption to main

wash water consumption reveals that this ratio is 1:3,5 no matter whether the detergent

dosage is nominal, under- or overdosed. Assumption that the fuzzy logic would adapt

to the water consumption if the amount of detergent is overdosed is not met.

A possible explanation for this might be because of the detergent used for washing

tests. In this case, the EN60456:2005 standard detergent A* is used, where the amount

DISCUSSION 62

of foam inhibitor is very high. For this reason it is possible that the foaming detection

sensors recognize that no further rinsing cycles are induced.

Another reason for this might be because of the washing program design of the tested

washing machines. It might be possible that the number of the rinsing cycles is not

sensor/fuzzy logic controlled and a standard number of rinsing cycles is included in

the washing process.

Energy consumption

In a washing machine, energy is consumed for heating the water, running the main

motor and the drain pump, as well as for running the sensors, control processor and

other electronic signaling units of the washing machine. During the washing machine’s

test, the energy consumption of the processor and signaling unit were not measured.

About 91,9 % in the variation in energy consumption can be explained by independent

variables: actual washing temperature, water consumption, and the duration of the

main wash. This coefficient of determination value can be considered to be good when

the afore-mentioned variances among different washing machines are taken into

account.

The most energy, however, is consumed for electrically heating up the water,

especially when the inlet water is not preheated (KUTSCH et al., 1997; SMULDERS,

2007; WAGNER, 2011). This is also confirmed by the data received in the washing

machine’s test. With an increase of the temperature, the energy consumption increases.

The higher the washing temperature, the more energy is needed to heat up the water,

hence more energy is consumed. (SMULDERS et al., 2007)

The data, however, shows differences in energy consumption among different washing

machines and different load sizes. Since the water consumption strongly depends on

load size, the energy consumption indirectly also depends on the load size, so that the

dependencies discussed in 5.1.1 also apply here.

DISCUSSION 63

Furthermore, the data shows that some washing machines do not always heat up the

water to the preset temperature (Table 4-5). For example, WM9 reaches, in a nominal

60 °C program, a maximal actual washing temperature of 47 °C.

The reason for this might be in the specification given in the Eco-design regulation.

According to it, the washing machines have to reach a certain washing performance

index. In a reference 60 °C program, a washing index of 1,03 is required. In practice

this means that the manufacturers have to provide one program for each washing

machine that fulfills those conditions, and this program is to be used when the washing

machine is tested. It is up to the manufacturer to find a perfect combination of the

washing factors so that the required washing performance can be achieved. In order to

save energy, some washing machine producers lower the actual washing temperature

and increase the main washing time. VAN HOLSTEIJN EN KEMNA, 2013 also observed this

trend of prolonging the washing cycle in washing machines, which “opens the

possibility to wash with lower temperatures with the same washing result and can

therefore increase the energy efficiency.” (VHK, 2013, p.41) They see a risk in those

longer cycle times, in such that “people might not use the energy efficient program but

will use the normal cotton program instead. They might simply not have the time to

wait for 5 hours before they can start the next load.” (VHK, 2013, p.41)

Due to this discrepancy between the nominal washing temperature and the actual

washing temperature, in the energy equations the maximally reached actual washing

temperature is included. With beta values of 0,72 (Table 3-1), the washing

temperature’s contribution to the explanation of the variance is the highest. The

contribution of the duration of the main wash is roughly as high as the contribution of

the water consumption to the explanation of the variance.

Analogous to the water consumption, the real energy consumption also depends on

what the manufacturer wants to offer to the consumer as a standard washing program.

In some cases, the manufacturer offers a shorter program as standard and the consumer

has to use the “intense” or “stains” option to extend the program. Other manufacturers

DISCUSSION 64

offer a longer washing program as a standard program, and the consumer has to

choose the “express” or “short” option in order to shorten the washing program.

However, under certain circumstances, even the duration of the main wash has more

influence on the energy consumption than the chosen temperature. In those cases

when a low washing temperature and long duration of the main wash is selected

(which are, for example, the case in an eco-program), the share of the energy

consumed for heating up the water is lower than the share of energy consumed by the

engine.

In the equation eq. (4-4), the constant (y-intercept) is -0,836 kWh. Assuming that all

the explanatory variables are set to 0, the value of the response variable would be

negative. However, as seen in the equation for the calculation of water consumption,

4,01 L of water is always consumed, and its temperature is higher than 0 °C in order to

be even in fluid state and hence added to the washing machine. For this reason the

validity of the equation is limited.

5.1.3 Detergent consumption

The equation for calculating the detergent consumption is based on a multiple

regression that sets the washing performance in relation with the load size, duration of

the main wash, actual washing temperature and amount of detergent. The final

equation is then developed by solving the equation for detergent.

Consumer behavior influences the outcome of a washing cycle. In a washing cycle, the

consumer has to decide (1) how much of laundry to load, (2) what washing program

(temperature, duration, spinning speed, rinsing characteristics) to choose, and (3) what

kind of detergent to use and how much to dose. In the equation, some of those

consumer patterns are included, and each can be adjusted separately.

The washing machine test data shows that the load size has a strong impact on the

washing performance (Figure 4-6). The beta values of the multiple regression analysis

(Table 4-9) show that the load size actually has the highest impact on the washing

DISCUSSION 65

performance index of all variables. A negative B value of the load (-0,034584)

indicates that every ceteris paribus increase of the load size results in a decrease of the

washing performance index.

In accordance with SINNER 1961, with a lower load size the free space in the drum

increases and hence the laundry has more space to tumble down, increasing the

mechanical force. When all other factors are kept constant, an increase of the washing

performance index occurs.

The virtual washing machine offers a set of input parameters (e.g. load amount,

washing temperature, etc.), in which each can be individually varied. Only those

variables that can actually be chosen by the consumer are included in the model. A

washing program can be simulated by combining/varying those parameters.

- Temperature: Washing temperatures between 30 °C and 60 °C can be preset in

the equation. As previously discussed in chapter 5.1.2, there is a discrepancy

between the nominal and actual washing temperature.

- Duration of the main wash: In real-life washing machines, there is not a

possibility to preset the duration of the main wash in the same manner as is

possible with the washing temperature or the spin. However, with time

consumers learn which washing programs are short and which are long, and

choose the respective program when needed. By doing so, consumers are able

to influence the duration of the washing program. Another possibility to

influence the duration of the main wash is by choosing other options, such as

the “short” or “intense” option.

There are also some limitations of this “virtual program” in comparison to the real-life

washing programs. For example, some washing programs interrupt the main wash

program with spinning cycles in order to facilitate the wetting process. However, in

this virtual washing machine those specific program designs are not implemented.

The data shows that with a decrease of the average washing temperature, the washing

performance index decreases. This fact also shows that there is a potential for tradeoff

DISCUSSION 66

between the washing temperature and the washing performance. Furthermore, there is

also interdependency between the washing performance and the amount of detergent

(Figure 4-8). This bears a potential for a tradeoff for consumers who have to wash

textiles for which a lower washing performance is acceptable (e.g. slightly soiled

textiles). In those cases, where there is no need to have a high washing performance,

consumers can lower the washing temperature and thus save energy. Alternatively for

the consumer, there is a possibility to lower the amount of detergent.

5.2 Virtual washing household

The second goal of the present work is to develop a model of a household washing

behavior that incorporates the household- and behavioral parameters.

The mathematical model presented in 3.2.3 differs from models presented by other

research because it does not calculate with a fixed load size, but rather varies during

the simulation in accordance with model settings. Furthermore, it allows a variation of

certain parameters such as: number of persons in household, amount of load per

person, and shares of different program/temperature batches of the total amount of

load.

An important variable in the model of the virtual washing household is the loading

factor (Figure 4-12).

Most of the mathematical models presented by other researchers are very simple, and

in most cases the calculations are done by assuming the consumer uses a full load (or

possibly a half load). A loading factor, which depends on the type of laundry, is not

included in such calculations.

The consumers, however, do not use the full capacity of the washing machine.

According to STAMMINGER, 2011, 10 % of consumers in Europe load the washing

machine in dependence of the type of laundry, and 63 % load the washing machine

without overloading it. Consumer associations also promote fully loading the washing

machine, except when washing delicates, in which case a half load is recommended

(ForumWaschen, 2013). By using the virtual washing household model, it is possible

DISCUSSION 67

to include all different loading factors that differ in dependence of the type of laundry

that has to be washed.

The key moderating parameter in this simulation design is the maximal laundry

waiting time parameter (Figure 4-13).

With this variable, it is possible to add a time dimension to the virtual washing

household model and so explore the effects that might occur when the consumer is

ready to postpone an action (in this case the washing of laundry) to a later point of

time. For example, “being in hurry” can be simulated by shortening the MLWT and

“environmentally conscious” behavior can be simulated by extending the maximal

laundry waiting time.

In this work, it is possible to simulate the usage of a washing machine, in a parallel

manner, by households of different sizes. Comparison of the usage of the same

washing machine by households (of up to seven persons) helps to reveal how the

household size influences the consumption.

5.3 Simulations

The third goal of the present work is to develop and conduct simulation of the usage of

the virtual washing machine by the virtual washing households. By conducting parallel

simulations and by varying device-, household- and behavioral parameters, a

parameter combination with the lowest environmental impact can be determined, and

hence conclusions regarding an optimal consumer behavior can be drawn. In order to

answer this question, the following aspects are examined:

- Washing machine’s rated capacity

- Resource consumption

- Washing frequency

- Average washing temperature

DISCUSSION 68

5.3.1 Washing machine’s rated capacity

Environmental impact of automatic washing depends not only on the household size

and the washing behavior (i.e. maximal laundry waiting time), but also the washing

machine’s rated capacity.

Comparison of the slopes in Figure 4-14 shows that when the maximal laundry waiting

time is very low, households with a lower number of persons would have the lowest

impact on the environment when using a washing machine with a lower rated capacity.

A washing machine of 8 kg or even 11 kg would be less adequate in such a case.

With an increase of the number of persons in the household, a washing machine with a

higher rated capacity becomes more adequate. With an increase of the maximal

laundry waiting time, the amount of laundry that accumulates in a household increases

further. In such a case, a washing machine with a higher rated capacity becomes more

adequate due to use of the economies of scale potential of such washing machines

(Figure 4-15 - Figure 4-17).

A usage of washing machines with a higher rated capacity by smaller households (for

example 1-3 person households) demands change of the behavioral patterns. One

possibility is that the household increases the maximal laundry waiting time and waits

until enough laundry is accumulated so that the rated capacity of the washing machine

can be used. However, it is questionable whether the consumers are willing to wait for

three or more weeks until enough laundry is accumulated.

Another possibility for more sustainable usage might be in combining the different

washing loads. For example, by combining a 30 °C and 40 °C washing load, and by

choosing the washing temperature in accordance with the recommendation of the most

sensitive laundry piece, and in combination with prolonging the washing cycle, the

same washing performance at a lower resource consumption may be achieved.

(JUNGBLUTH et al., 2006) as well as (JANCZAK et al., 2010) have already shown that

lowering the temperature to the next possible level and prolonging the washing time

leads to a decrease in the energy consumption while the washing performance remains

the same.

DISCUSSION 69

It should also be kept in mind that a continuous usage of lower temperatures may

generate some other issues, such as problems with hygiene. In such cases, the usage of

appropriate washing detergent, such as a bleach-containing detergent, could help to

resolve those issues. Furthermore, in this respect, the consumer should consider

following the suggestions of the consumer association to cyclically wash at higher

temperatures, and to leave the door and the detergent container open after a washing

cycle.

5.3.2 Resource consumption

The resource consumption comparison shows that energy and detergent have the

highest share in CO2 emission. The share of the water is not very high. The reason for

this is that the water consumption has been optimized in the past decades. According

to the EuP preparatory study, water consumption has reduced from 1997 to 2005, with

an annual improvement of 0,28 l / kg (PRESUTTO et al., 2007).

The contribution of the energy to the total CO2 emission is highest when a smaller

household uses a washing machine with a higher rated capacity and the maximal

laundry waiting time is very short. In such a case, the household never uses the rated

capacity of the washing machine as shown in the model. With a lower amount of load,

the mechanical work is increased (SINNER, 1961) and therefore a lower detergent

dosage is needed to reach the preset washing performance. In such cases, the energy

related CO2 equivalent emission is more than three times higher than the CO2

equivalent emission of detergent (Figure 4-19).

For the calculations of the energy consumption, it was assumed that an energy mix is

used, where a certain portion is produced using nuclear-, coal- and renewable energy

sources. In this case, the CO2 equivalent conversion factor for energy is 600 g / kWh.

In an energy mix with a lower CO2 equivalent emission (which might come in the

future), the detergent consumption might have a higher influence on the CO2 emission

than the energy consumption.

DISCUSSION 70

On the other hand, it is also possible that more advanced technology in detergent

production might occur, which then could lead towards a lower impact of the detergent

on the CO2 equivalent emission.

In both cases, there is a potential for tradeoff between the energy and the detergent that

could be used in order to have an even more sustainable behavior.

5.3.3 Washing frequency

The time consumed for washing purposes is also an important factor when evaluating

the different washing behavior patterns. Although the time consumed for washing

purposes includes the washing machine loading/unloading time as well as the washing

time, in this research the washing frequency solely represents time consumption.

One of the arguments for selling washing machines with a large load capacity is that it

is time saving, since more loads can be washed at the same time, hence the consumers

are able to reduce the number of washing cycles when using those washing machines.

However, this depends on the household size and the washing machine’s rated

capacity, as well as on the consumer behavior when using the washing machine.

As presented in the data (Figure 4-20), two different washing cycles are to be

distinguished. The first one is a “normal washing cycle”, in which the washing

machine’s rated capacity (adjusted for the load factor) is fully utilized. The second one

is an “emergency washing cycle”, in which the washing cycle is conducted no matter

how much laundry is loaded.

The behavioral factor that influences the frequency of emergency washing cycles is the

maximal laundry waiting time. For example, when the maximal laundry waiting time

is “up to 7 days” there is almost no difference if consumers use 5 kg, 8 kg or even

11 kg washing machine in cases of one-person and two-person households. In all those

cases, the washing machine’s rated capacity is not fully used, which means that most

of the washing cycles are “emergency washing cycles” and hence no time is saved.

DISCUSSION 71

With an increase of the household size, the number of emergency washing cycles

decreases, and the differences among washing machines regarding the washing

frequency becomes more visible.

An exception is the 11 kg washing machine, in which the emergency washing cycles

also occur in the case of a 7-person household, when the maximal laundry waiting

time is low. The reason for this is that even a 7-person household does not have

enough 60 °C laundry which can be collected in such a short time in order to use the

rated capacity of the washing machine. However, with an increase of the maximal

laundry waiting time, the number of emergency washing cycles decreases here as well.

The consequences of the washing frequency are manifold:

- Firstly, high usage frequency might be stressful for the consumer. For example,

those consumers whose work life allows washing only on weekends, or

consumers who use a community washing machine, might experience the high

washing frequency as very tedious. It is possible that in such cases those

consumers, in order to be able to wash more washing cycles in a shorter period

of time, chose to use a shorter washing program that might be inadequate from

the resource consumption, hygienic or/and general cleanliness perspective.

- Secondly, with an increased frequency, a fatigue of material in the washing

machine, such as the drum casing bumpers, could undergo damage, as well as a

lower life expectancy of the washing machine. ÖKO-INSTITUT calculates that a

washing machine can be used for 1840 washing cycles (RÜDENAUER et al., 2004).

A non-optimal usage might shorten the life expectancy of a washing machine.

5.3.4 Average washing temperature

Data received from the washing machine test and the eq. (4 4) show that the energy is

directly correlated with the chosen washing temperature. In order to lower energy

consumption, washing at lower temperatures is promoted. Since the early 1970s, the

DISCUSSION 72

average washing temperatures have declined. This is a good indicator for the

promotion of a more environmentally friendly behavior (AISE, 2013).

As the data shows (Figure 4-21), the influence of the maximal laundry waiting time on

the average washing temperature is also highly important. With an increase of the

laundry waiting time, the rated capacity of the washing machine is used more often

(which is especially the case of the 60 °C washing cycles, in which the loading factor

is highest), and hence the average washing temperature decreases. This finding is of

high importance for two reasons:

Firstly, consumer associations’ promotion of lower temperatures has often encountered

a resistance among consumers, due to probable perceived washing performance

inefficiency. A possible solution to this problem might be in promoting the extension

of the maximal laundry waiting time, which then would induce the use of the washing

machine’s rated capacity, and therefore might lead to a decrease of the average

washing temperature.

Secondly, those consumers who do not wish to decrease the washing temperature due

to fear of hygiene-related issues can, at least to some extent, contribute to

environmental protection by increasing the maximal laundry waiting time and so lower

the household average washing temperature.

Furthermore, the data shows that the effects of decreasing the average washing

temperature while increasing the maximal laundry waiting time occur in dependence

of the washing machine size and the household size.

For example, a washing machine with a 5 kg rated capacity used by a 1-person

household has a potential to lower the average washing temperature when increasing

the maximal laundry waiting time. However, usage of an 11 kg washing machine by a

1-person household does not bear such a potential.

It can be concluded that there is an optimal washing machine’s rated capacity for each

household size.

DISCUSSION 73

5.4 Deficits of the presented research

Although the virtual washing machine and the virtual washing household display a

robust predicting power, there are still some aspects which should be critically

observed.

In the present work, the hygiene and the soil re-deposition were not a subject of

research. However, those aspects should also be considered, because the hygiene

aspect in laundry washing, as well as the soil re-deposition, is increasing, which is

something the consumers are able to evaluate at the end of a washing cycle. This could

induce a behavioral change towards rewashing the laundry, or usage of less resource-

saving programs, such as intense or water plus programs.

The duration of the main wash in the virtual washing machine model is automatically

selected in dependence of the washing machines’ rated capacity. The values used are

average values of the individual washing machines groups. Such an approach is

certainly valid in order to predict the resource consumption of tested washing

machines. However, generalizing all washing machines should be used with caution.

In the virtual washing machine, the rinsing cycles are not part of the model and a

constant factor is included instead. Such a simplified approach allows for an

estimation of the resource consumption, but the model does not allow the inclusion of

consumer behavior in usage of rinsing options (water plus option or sensitive option).

An inclusion of the rinsing cycles in the model would enhance the model and make it

more precise in predicting the water consumption.

The virtual washing machine model allows for the prediction of energy consumed

during the main wash. However, although the share of energy consumed during the

rinsing phase and spinning phase, as well as in the standby mode, is not as high as the

energy consumed during the main wash, its inclusion in the virtual washing machine

model would allow a more precise prediction of the resource consumption.

Detergent used for the washing machine test is A* detergent as specified by

EN60456:2005. Usage of A* detergent helps to compare the washing machines.

Commercially available detergents used by consumers, however, differ significantly.

DISCUSSION 74

Commercial detergents contain advanced components (e.g. enzymes) that allow lower

dose amounts without lowering the washing performance. Inclusion of the commercial

detergent in the model would provide an even more precise picture of the influence of

washing on the environment.

In the case of the virtual washing household, it was assumed that the household

displays an ideal behavior when washing laundry. However, this is not the case in real

life. Such an approach only helps to illustrate the real-life behavior to some extent.

There are many parameters that influence the consumer behavior such as the gender

age of the participants or even seasonal aspects which are not included in this research.

An inclusion of those aspects would help to explore the consumer behavior more

thoroughly.

CONCLUSION AND FUTURE PROSPECTS 75

6 Conclusion

Based on the data of different washing machines, a model of a virtual washing

machine was developed and, despite the large differences among the tested washing

machines, it shows a robust predicting power.

The developed virtual washing household model offers a high range of possibility to

simulate some of the consumers’ behavioral patterns. Its dynamic development of the

laundry stock course and possibility to vary household and washing machine

parameters makes this model more advanced in comparison to the modeling solutions

described in the literature.

The virtual washing household model, used in this work, could also be used to

simulate usage of other household appliances. For example, the virtual washing

household model could also be used to simulate the usage of automatic dishwashers or

tumble dryers. By extending the model to more household appliances, a more precise

resource consumption-predicting model could be established and used to predict the

daily energy consumption and possible peak demands. Furthermore, it would enable a

simulation of more precise trend developments so the policy making decision could be

supported.

In connection with this, the variable maximal laundry waiting time has proven to be of

great help in adding a “sustainability component” to the consumer model. At present,

no research has evidenced the usage of such a moderating factor that has been

presented in the literature. The maximal laundry waiting time parameter is of great

value due to its ease of use and its ability to support the increasing value of

information that must retrieved in behavior-related simulations. Furthermore, it

reflects real-life behavior very well, in cases where consumers do not wash the same

amount of laundry every week, but instead only wash when there is enough laundry or

when certain loads have to be washed, regardless of whether the rated capacity is fully

used or not (emergency washing cycle). For this reason, it should be included as an


eventual standard variable in future models where the sustainability-related consumer

behavior is being explored.


7 Future prospects

In the present study, the amount of laundry added to the washing machine, as well as

the rated capacity of the washing machine are each expressed in kilograms. However,

in some cases the declared increase of the washing machine’s rating capacity is not

followed by a volume increase of the washing machine’s drum. In such cases, the

resulting overload of the drum and consequent decrease of mechanical work (Sinner,

1960) is substituted by an increase of the other washing factors, such as the washing

time.

For this reason, in a follow-up model of the virtual washing machine, it would be

important to include the washing machines’ drum volume instead of the rated capacity

expressed in kilograms.

Furthermore, in the following study the actual washing temperature is measured in the

sump (that is, in the space in-between the inner and outer drum at the bottom of the

washing machine’s drum). This temperature is not necessarily the same as the

temperature in the load (especially at the spot where textile, soil and wash liquor

meet). However, the temperature at this spot is actually influencing the washing

performance. An inclusion of this parameter in a future follow-up virtual washing

machine model should be considered so that it might lead to a more accurate model.

In addition, for the washing machines’ tests, cotton was used as a test load. In an

eventual follow-up model, other fabrics such as synthetics should also be included as

test loads. This would allow for a more realistic, real-life relevant model. Additionally,

in the follow-up model other detergents (liquor detergent, commercial detergent, etc.)

and new washing machines models (e.g. models that use heat-pump and/or automatic

dosing) should be included.

The virtual washing household model also bears potential for improvement. In the

presented study it was assumed that the consumer washes every temperature-specific

load separately. In real life, however, this is not the case and the consumers often

combine two (temperature/program) batches of laundry together. A possible


optimization of the model should also include such behavioral patterns in the

simulation.

Furthermore, additional research is necessary to identify the driving forces of non-

optimal consumer behavior (e.g. lack of time, habit to wash every week, convenience

of always having clean laundry or to wash on specific weekdays). The findings of such

research could, together with findings of this study, be used to develop practicable

tools that stimulate more sustainable behavior. Such tools could be used to promote the

increase of the maximal laundry waiting time. For example:

Consumer associations could develop an info brochure informing

consumers to separate the laundry not only by the color/soil level or type

of textile, but to also include a “washing priority” category.

Consumer associations could develop advising tools (e.g. checklist)

which could be used as guidance when buying a new washing machine.

Industries could develop information systems on the washing machine

that promotes the extension of the maximal laundry waiting time (e.g.

washing frequency tracker, sticker/dosing ball that changes color when

used too frequently.

Detergent manufacturers could develop products that encourage a longer

usage of textiles.

REFERENCES 79

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ABBREVIATIONS 85

9 Abbreviations

°C Degree celsius

ca. circa

e.g. exempli gratia

et al. et alteri

g Gramm

h Hour

Hz Hertz

i.e. Id est

kg Kilogram

kWh Kilowatt-hour

L Liter

mg Milligramm

min Minute

MLWT Maximal laundry waiting time

mmol / L Millimol per liter

p. Page

resp. Respectively

s Second

V Volt

W Watt

WM Washing machine

WMRC Washing machine rated capacity

Arithmetic mean

ABBREVIATIONS 86

FIGURES 87

10 List of figures

Figure 1-1: Sinner’s circle (own representation)...................................................................... 1

Figure 1-2: Sinner's circle for washing by hand (own representation) ..................................... 2

Figure 1-3: Sinner's circle for washing in a washing machine at a high temperature

(own representation) .......................................................................................... 2

Figure 1-4: Average load capacity trends of household washing machines ............................ 7

Figure 1-5: Life cycle analysis of a washing machine (Source: RÜDENAUER et al.) .................. 9

Figure 1-6: Contribution of the different sectors to the GHG emission

(Source: EEA, 2010) ........................................................................................ 13

Figure 3-1: Example of a testing procedure on an example of a 25 % load .......................... 23

Figure 3-2: Schematic of the mathematical model construction of a washing

machine (own representation) .......................................................................... 28

Figure 3-3: Example of a schematic of the simulation ........................................................... 29

Figure 4-1: Water consumption versus load of nine washing machines - washing

temperature is 60 °C ........................................................................................ 33

Figure 4-2: Comparison between the specific water consumption and the load size ............. 34

Figure 4-3: Water consumption versus load amount - washing temperature is 60 °C ........... 34

Figure 4-4: Energy consumption in kWh versus load size. Washing temperature

60 °C ............................................................................................................... 36

Figure 4-5: Energy consumption versus load amount - washing temperature is 60 °C ......... 36

Figure 4-6: Index of washing performance versus load for cotton 60 °C ............................... 37

Figure 4-7: Index of washing performance vs. energy consumption. From left to right

the energy values indicate the machines' energy use for 30 °C, 40 °C

and 60 °C washing programs ........................................................................... 38

FIGURES 88

Figure 4-8: Index of washing performance vs. detergent dosage .......................................... 39

Figure 4-9: Schematic of a construction of a virtual washing machine based on the

data received during the washing machine tests .............................................. 41

Figure 4-10: Example of 30 °C laundry stock course in a 5 kg rated capacity

machine when 1 kg of laundry is added to the stock every week ..................... 47

Figure 4-11: Laundry stock course with three different washing

programs/temperatures in a 5 kg rated capacity washing machine

when every week 1 kg, 2 kg and 3 kg of laundry are added to the 30

°C, 40 °C and 60 °C laundry stock ................................................................... 48

Figure 4-12: Impact of the loading factor (implemented in the case of 30 °C load) on

the laundry stock course of three different washing

programs/temperatures in a 5 kg rated capacity washing machine

when every week 1 kg, 2 kg and 3 kg of laundry are added to the 30

°C, 40 °C and 60 °C laundry stock. .................................................................. 48

Figure 4-13: Impact of the maximal laundry waiting time (implemented in all three

programs/temperatures) on the laundry stock course of three different

washing programs/temperatures in a 5 kg rated capacity washing

machine when every week 1 kg, 2 kg and 3 kg of laundry are added to

the 30 °C, 40 °C and 30 °C laundry stock.The impact is visible in the

reduction of the laundry stock in the third week. ............................................... 49

Figure 4-14: Comparison of emission of CO2 equivalents when MLWT = 0 .......................... 50

Figure 4-15: Comparison of emission of CO2 equivalents when MLWT = 1 ......................... 51



Figure 4-18: Overview of washing machine’s rated capacity vs. MLWT ................................ 54

Figure 4-19: CO2 equivalents emission split by individual resource consumption

share................................................................................................................ 55

FIGURES 89

Figure 4-20: Time exposure in dependence of the rated capacity, household size

and MLWT (Starting with maximal laundry waiting time of “up to 1

week” (Chart a) to maximal laundry waiting time of “up to 4 weeks”

(Chart d)). ........................................................................................................ 56

Figure 4-21: Comparison of average washing temperatures in dependence of the

household size: 1-person household (4-21a) – 4-person household (4-

21d) ................................................................................................................. 57

FIGURES 90

TABLES 91

11 List of tables

Table 3-1: List of tested washing machines .......................................................................... 19

Table 3-2: Equipment used for the normalization process of the load ................................... 20

Table 3-3: Soil strips and respective batch number used for the tests .................................. 20

Table 3-4: Detergent ............................................................................................................ 20

Table 3-5: Overview of software used .................................................................................. 21

Table 3-6: Loading scenarios for different washing machines’ rated capacities .................... 21

Table 3-7: Test design and variation of the parameters ........................................................ 22

Table 3-8: CIE Y values of different batches and extrapolated values for 110g of

detergent ......................................................................................................... 25

Table 3-9: Testing conditions in accordance with EN60456:2005 ......................................... 25

Table 3-10: Overview of CO2 conversion factors.................................................................. 26

Table 4-1: Ratio between the main wash water consumption and the total water

consumption (mean values), in dependence of the detergent dosage .............. 35

Table 4-2: Ratio between the main wash water consumption and the total water

consumption (mean values), in dependence of the load size ........................... 35

Table 4-3: Overview of average washing performance achieved at different washing

temperatures .................................................................................................... 39

Table 4-4: Overview of different average durations of the main wash at the following

washing temperatures ...................................................................................... 40

Table 4-5: Overview of average actual washing temperatures that were achieved

instead of the respective nominal temperature ................................................. 40

Table 4-6: Summary statistics, correlations, and results from the regression analysis

(water consumption) ........................................................................................ 42

TABLES 92

Table 4-7: Summary statistics, correlations and results from the regression analysis

(energy consumtion) ........................................................................................ 43

Table 4-8: Correlations between predictor variables ............................................................. 44

Table 4-9: Summary statistics, correlations and results from the regression analysis

(detergent consumption) .................................................................................. 45

Table 4-10: Collinearity statistics .......................................................................................... 45

Appendix

Table 12-1: Indesit IWB 5125) 5 kg rated capacity .................................................................. I

Table 12-2: Miele W1514WPS, 5 kg rated capacity ................................................................ II

Table 12-3: BOSCH maxx 6 Eco Wash, 6 kg rated capacity ................................................. III

Table 12-4: AEG Öko Lavamat 76850, 7 kg rated capacity .................................................. IV

Table 12-5: Bauknecht WA UNIQ 714FLD, 7kg rated capacity ............................................. VI

Table 12-6: BoschLogix8, 8kg rated capacity ...................................................................... VII

Table 12-7: Haier HWF1481, 8 kg rated capacity ............................................................... VIII

Table 12-8: INDESIT PWE 8168W, 8kg rated capacity ........................................................ IX

Table 12-9: Bauknecht WAB-1210, 11kg rated capacity……………….................………… X

APPENDIX I

12 Appendix

12.1 Washing machine tests data

Table 11-1: Indesit IWB 5125) 5 kg rated capacity

Lo

ad s

ize

in k

g

Det

erg

ent

do

sag

e

in g

En

erg

y

con

sum

pti

on

in

kW

h

En

erg

y

con

sum

pti

on

in

mai

n w

ash

in

kW

h

Wat

er

con

sum

pti

on

in L

Wat

er

con

sum

pti

on

mai

n w

ash

in

L

Wat

er

con

sum

pti

on

rin

se c

ycl

e i

n L

Du

rati

on

sp

inn

ing

ph

ase

in m

in

Du

rati

on

mai

n

was

h

in m

in

Was

hin

g

tem

per

ature

in

°C

Was

hin

g

Per

form

ance

Ind

ex

5,00 50 0,42 0,31 63,7 15,5 48,2 29 41 30 0,81

5,00 50 0,82 0,73 40,8 16,6 24,3 39 84 40 0,91

5,00 50 0,94 0,85 39,5 16,5 23,0 21 84 60 0,94

5,00 100 0,43 0,34 64,0 16,5 47,5 18 41 30 0,87

5,00 100 0,80 0,71 40,5 16,8 23,7 16 84 40 0,98

5,00 100 0,95 0,86 40,8 17,1 23,8 16 83 60 1,00

5,00 150 0,45 0,34 64,4 16,1 48,4 17 41 30 0,90

5,00 150 0,79 0,70 40,4 16,7 23,8 16 83 40 1,02

5,00 150 0,92 0,84 38,6 17,0 21,6 16 83 60 1,04

3,75 42,5 0,40 0,30 58,7 13,6 45,1 17 40 30 0,82

3,75 42,5 0,74 0,65 36,3 14,5 21,8 16 83 40 0,93

3,75 42,5 0,94 0,85 36,9 13,8 23,1 16 82 60 0,97

3,75 85 0,39 0,30 58,2 13,5 44,7 16 40 30 0,90

3,75 85 0,73 0,64 35,8 14,3 21,5 16 82 40 1,01

3,75 85 0,91 0,83 35,7 14,0 21,6 16 82 60 1,04

3,75 127,5 0,41 0,31 58,6 13,7 44,9 16 39 30 0,94

3,75 127,5 0,68 0,60 34,9 14,1 20,9 16 82 40 1,04

3,75 127,5 0,90 0,82 33,3 14,4 18,9 19 83 60 1,08

2,50 35 0,36 0,28 54,0 11,3 42,6 17 38 30 0,85

2,50 35 0,67 0,59 30,8 12,1 18,8 16 81 40 0,96

2,50 35 0,88 0,80 30,6 11,4 19,2 16 81 60 0,98

2,50 70 0,35 0,26 53,9 11,2 42,7 16 38 30 0,92

2,50 70 0,62 0,55 29,7 11,2 18,5 16 81 40 1,02

2,50 70 0,92 0,84 30,2 11,5 18,6 15 81 60 1,07

2,50 105 0,34 0,25 52,9 10,9 42,1 16 38 30 0,96

2,50 105 0,65 0,57 30,5 11,5 18,9 16 81 40 1,07

2,50 105 0,91 0,83 30,2 11,5 18,7 15 81 60 1,13

1,25 27,5 0,30 0,21 46,3 7,7 38,6 17 37 30 0,91

1,25 27,5 0,54 0,46 23,0 8,7 14,4 15 80 40 1,01

1,25 27,5 0,79 0,72 23,3 8,3 15,0 15 81 60 1,05

APPENDIX II

1,25 55 0,31 0,22 47,3 7,7 39,7 16 37 30 0,98

1,25 55 0,56 0,48 22,8 8,1 14,7 15 81 40 1,08

1,25 55 0,76 0,68 22,3 8,4 13,9 16 81 60 1,13

1,25 82,5 0,30 0,22 45,0 7,5 37,5 16 36 30 0,92

1,25 82,5 0,53 0,46 23,9 8,8 15,1 21 81 40 1,12

1,25 82,5 0,75 0,68 21,6 8,0 13,6 19 81 60 1,17

0,00 0 0,23 0,16 38,6 5,3 33,4 16 35 30

0,00 0 0,38 0,32 16,7 5,7 11,0 25 80 40

0,00 0 0,54 0,48 16,3 5,8 10,4 15 81 60

Table 11-2: Miele W1514WPS, 5 kg rated capacity

Load

siz

e in

kg

Det

ergen

t

dosa

ge

in g

En

erg

y

con

sum

pti

on

in

kW

h

En

erg

y

con

sum

pti

on

in

main

wash

in

kW

h

Wa

ter

con

sum

pti

on

in L

Wa

ter

con

sum

pti

on

main

wash

in

L

Wa

ter

con

sum

pti

on

rin

se c

ycl

e i

n L

Du

rati

on

spin

nin

g p

ha

se

in m

in

Du

rati

on

main

wash

in

min

Wash

ing

tem

per

atu

re i

n

°C

Wash

ing

Per

form

an

ce

Ind

ex

5,00 50 0,29 0,20 42,8 13,1 29,7 8 52 30 0,93

5,00 50 0,31 0,21 53,0 13,1 39,9 10 71 30 0,88

5,00 50 0,46 0,36 51,9 12,7 39,2 9 71 40 0,95

5,00 50 0,79 0,69 40,8 12,3 28,5 9 63 60 0,98

5,00 100 0,36 0,25 47,8 13,1 34,7 9 71 30 0,99

5,00 100 0,32 0,22 54,6 14,4 40,2 9 71 30 1,00

5,00 100 0,44 0,36 44,2 13,4 30,8 8 52 40 1,02

5,00 100 0,45 0,35 53,0 13,3 39,7 9 71 40 1,00

5,00 100 0,79 0,70 40,8 12,8 28,0 9 63 60 1,06

5,00 150 0,27 0,19 39,8 13,2 26,6 8 52 30 1,01

5,00 150 0,31 0,21 54,3 14,2 40,1 10 71 30 0,97

5,00 150 0,50 0,39 49,8 12,9 36,9 9 71 40 1,06

5,00 150 0,79 0,71 40,5 13,0 27,5 9 63 60 1,13

3,75 42,5 0,22 0,14 32,5 9,7 22,8 8 52 30 0,89

3,75 42,5 0,34 0,27 35,1 11,0 24,1 8 52 40 0,93

3,75 42,5 0,65 0,57 39,6 10,8 28,8 8 52 60 0,97

3,75 85 0,21 0,13 39,5 11,0 28,5 8 52 30 0,95

3,75 85 0,37 0,27 39,6 10,7 28,9 8 52 40 1,00

3,75 85 0,66 0,59 40,4 11,0 29,4 8 52 60 1,05

3,75 127,5 0,23 0,15 39,8 11,5 28,3 8 52 30 1,00

3,75 127,5 0,35 0,27 39,9 11,0 28,9 8 52 40 1,02

3,75 127,5 0,67 0,60 39,4 10,9 28,5 8 52 60 1,12

2,50 35 0,21 0,14 31,7 9,3 22,4 8 52 30 0,94

2,50 35 0,38 0,30 34,4 10,6 23,8 8 52 40 0,98

2,50 35 0,70 0,62 36,7 10,3 26,4 8 52 60 1,03

APPENDIX III

2,50 70 0,24 0,16 34,1 10,6 23,5 8 52 30 1,00

2,50 70 0,38 0,31 33,9 10,6 23,3 8 52 40 1,05

2,50 70 0,72 0,64 37,5 11,0 26,5 8 52 60 1,12

2,50 105 0,23 0,15 32,5 9,7 22,8 8 52 30 1,04

2,50 105 0,35 0,27 32,9 10,2 22,7 8 52 40 1,08

2,50 105 0,69 0,62 35,8 10,1 25,7 8 52 60 1,17

1,25 27,5 0,17 0,11 20,2 6,7 13,5 5 52 30 0,98

1,25 27,5 0,25 0,19 19,8 6,5 13,3 5 52 40 1,02

1,25 27,5 0,49 0,43 20,2 6,4 13,8 5 52 60 1,06

1,25 55 0,17 0,10 20,0 6,8 13,2 6 52 30 1,05

1,25 55 0,26 0,20 19,7 6,5 13,2 5 52 40 1,01

1,25 55 0,49 0,43 19,7 6,5 13,2 5 52 60 1,14

1,25 82,5 0,17 0,10 25,2 6,4 18,8 6 52 30 1,08

1,25 82,5 0,14 0,12 22,2 7,6 14,6 6 52 30 1,08

1,25 82,5 0,26 0,20 19,4 8,4 11,0 6 52 40 1,13

1,25 82,5 0,47 0,41 18,6 6,2 12,4 6 52 60 1,19

0,00 0 0,16 0,11 21,4 7,3 14,1 6 52 30

0,00 0 0,25 0,20 21,2 7,3 13,9 6 52 40

0,00 0 0,46 0,42 21,4 7,5 13,9 6 52 60

Table 11-3: BOSCH maxx 6 Eco Wash, 6 kg rated capacity

Load

siz

e in

kg

Det

ergen

t d

osa

ge

in g

En

ergy

con

sum

pti

on

in

kW

h

En

ergy

con

sum

pti

on

in

main

wash

in

kW

h

Wate

r

con

sum

pti

on

in L

Wate

r

con

sum

pti

on

main

wash

in

L

Wate

r

con

sum

pti

on

rin

se

cycl

e i

n L

Du

rati

on

sp

inn

ing

ph

ase

in

min

Du

rati

on

main

wash

in

min

Wash

ing

tem

per

atu

re i

n °

C

Wash

ing

Per

form

an

ce

Ind

ex

6,00 56 0,26 0,16 53,1 16,4 36,7 15 19 30 0,71

6,00 56 0,39 0,29 56,4 15,8 40,6 13 23 40 0,74

6,00 56 0,91 0,82 52,1 15,6 36,5 13 41 60 0,82

6,00 112 0,23 0,14 52,5 16,8 35,7 13 18 30 0,76

6,00 112 0,42 0,32 55,2 15,9 39,3 13 24 40 0,81

6,00 112 0,90 0,81 51,6 15,3 36,3 15 41 60 0,90

6,00 168 0,21 0,12 50,9 14,9 36,0 13 18 30 0,75

6,00 168 0,41 0,32 50,7 16,1 34,6 13 24 40 0,83

6,00 168 0,88 0,79 54,1 15,7 38,4 13 41 60 0,96

4,50 47 0,27 0,18 49,3 14,7 34,6 15 19 30 0,79

4,50 47 0,53 0,44 50,6 14,9 35,7 13 28 40 0,84

4,50 47 0,92 0,83 48,2 13,6 34,6 13 42 60 0,90

4,50 94 0,24 0,15 49,5 15,1 34,4 12 17 30 0,83

4,50 94 0,53 0,44 51,7 15,2 36,5 13 28 40 0,92

APPENDIX IV

4,50 94 0,90 0,82 47,8 13,5 34,3 13 42 60 0,98

4,50 141 0,24 0,15 49,2 14,9 34,3 13 19 30 0,87

4,50 141 0,51 0,42 49,5 15,2 34,3 13 28 40 0,94

4,50 141 0,94 0,85 49,0 14,0 35,0 13 43 60 1,04

3,00 38 0,27 0,18 45,8 11,7 34,1 13 19 30 0,79

3,00 38 0,46 0,37 46,6 12,2 34,4 12 26 40 0,86

3,00 38 0,78 0,69 45,4 10,8 34,6 13 37 60 0,93

3,00 76 0,29 0,19 45,9 11,9 34,0 16 20 30 0,87

3,00 76 0,44 0,35 46,3 11,8 34,5 13 25 40 0,92

3,00 76 0,81 0,73 45,8 11,2 34,6 13 37 60 1,01

3,00 114 0,24 0,15 46,4 12,2 34,2 13 18 30 0,91

3,00 114 0,47 0,39 46,4 12,0 34,4 13 27 40 0,96

3,00 114 0,75 0,67 45,4 10,7 34,7 12 36 60 1,07

1,50 29 0,24 0,14 43,0 8,6 34,4 14 18 30 0,82

1,50 29 0,40 0,30 43,0 8,7 34,3 12 24 40 0,93

1,50 29 0,66 0,57 42,7 8,3 34,4 14 33 60 0,97

1,50 58 0,24 0,14 43,5 9,1 34,4 16 18 30 0,91

1,50 58 0,39 0,30 43,6 9,0 34,6 14 24 40 0,97

1,50 58 0,64 0,55 42,7 8,2 34,5 12 33 60 1,04

1,50 87 0,24 0,14 43,4 8,9 34,5 16 17 30 0,93

1,50 87 0,38 0,30 30,0 9,1 20,9 15 24 40 0,99

1,50 87 0,42 0,32 42,4 8,9 33,5 14 24 40 1,09

1,50 87 0,63 0,54 42,6 8,1 34,5 13 32 60 1,00

0,00 0 0,18 0,11 22,9 5,6 17,3 12 17 30

0,00 0 0,27 0,20 22,8 5,5 17,3 12 21 40

0,00 0 0,43 0,36 22,0 4,7 17,3 12 27 60

Table 11-4: AEG Öko Lavamat 76850, 7 kg rated capacity

Lo

ad

siz

e in

kg

Det

ergen

t

do

sag

e in

g

En

erg

y

con

sum

pti

on

in

kW

h

En

erg

y

con

sum

pti

on

in

ma

in w

ash

in

kW

h

Wa

ter

con

sum

pti

on

in L

Wa

ter

con

sum

pti

on

ma

in w

ash

in

L

Wa

ter

con

sum

pti

on

rin

se c

ycl

e i

n L

Du

rati

on

spin

nin

g p

ha

se

in m

in

Du

rati

on

main

wa

sh in

min

Wa

shin

g

tem

per

atu

re i

n

°C

Wa

shin

g

Per

form

an

ce

Ind

ex

7,00 62 0,42 0,28 59,8 18,2 41,6 17 63 30 0,82

7,00 62 0,63 0,50 62,1 19,4 42,7 14 67 40 0,88

7,00 62 1,26 1,12 62,9 19,6 43,3 14 89 60 0,90

7,00 124 0,42 0,26 59,2 18,0 41,2 23 62 30 0,86

7,00 124 0,73 0,61 61,1 19,4 41,7 17 73 40 0,96

7,00 124 1,16 1,03 61,7 18,2 43,5 19 87 60 1,03

APPENDIX V

7,00 186 0,44 0,31 60,0 18,2 41,8 13 65 30 0,92

7,00 186 0,66 0,53 60,7 18,1 42,6 17 70 40 1,00

7,00 186 1,20 1,07 61,2 18,1 43,1 14 88 60 1,06

5,25 51,5 0,44 0,32 60,4 18,1 42,3 14 66 30 0,89

5,25 51,5 0,80 0,66 60,7 18,2 42,5 16 75 40 0,93

5,25 51,5 1,24 1,12 62,1 18,1 44,0 15 90 60 0,96

5,25 103 0,50 0,36 62,6 18,7 43,9 17 67 30 0,96

5,25 103 0,73 0,58 61,7 17,9 43,8 16 74 40 1,01

5,25 103 1,23 1,11 62,1 18,2 43,9 18 91 60 1,06

5,25 154,5 0,46 0,33 61,5 18,0 43,5 14 65 30 1,01

5,25 154,5 0,74 0,62 61,9 18,0 43,9 18 75 40 1,04

5,25 154,5 1,22 1,09 62,0 18,1 43,9 14 89 60 1,11

3,50 41 0,42 0,29 55,2 14,5 40,7 18 67 30 0,91

3,50 41 0,60 0,47 46,5 11,7 34,8 15 68 40 0,95

3,50 41 0,69 0,57 55,8 14,5 41,3 14 75 40 0,97

3,50 41 0,86 0,74 44,3 11,5 32,8 14 75 60 0,97

3,50 41 1,04 0,91 53,0 13,6 39,4 17 81 60 1,02

3,50 82 0,42 0,29 56,7 14,6 42,1 17 64 30 0,97

3,50 82 0,70 0,57 54,9 14,7 40,2 16 76 40 1,03

3,50 82 0,92 0,79 47,0 11,6 35,4 21 77 60 1,00

3,50 82 1,13 1,00 54,4 14,6 39,8 17 87 60 1,01

3,50 123 0,48 0,31 56,4 14,5 41,9 30 66 30 1,02

3,50 123 0,72 0,60 56,3 14,6 41,7 14 76 40 1,07

3,50 123 0,91 0,79 46,2 11,8 34,4 14 75 60 1,11

3,50 123 1,07 0,93 53,8 13,9 39,9 16 82 60 1,14

1,75 30,5 0,41 0,29 40,6 11,7 28,9 17 64 30 0,95

1,75 30,5 0,62 0,50 41,4 11,9 29,5 15 71 40 0,98

1,75 30,5 0,98 0,86 41,4 12,0 29,4 15 79 60 1,01

1,75 61 0,37 0,24 40,7 11,8 28,9 15 62 30 1,01

1,75 61 0,61 0,48 41,3 11,8 29,5 16 70 40 1,05

1,75 61 0,97 0,85 41,9 12,0 29,9 14 78 60 1,11

1,75 91,5 0,38 0,26 40,1 11,7 28,4 14 63 30 1,05

1,75 91,5 0,60 0,48 41,2 11,7 29,5 14 70 40 1,10

1,75 91,5 0,99 0,87 41,8 12,0 29,8 14 79 60 1,16

0,00 0 0,38 0,27 33,5 12,2 21,3 14 65 30

0,00 0 0,58 0,48 32,8 12,1 20,7 14 71 40

0,00 0 0,95 0,85 32,6 12,2 20,4 14 79 60

APPENDIX VI

Table 11-5: Bauknecht WA UNIQ 714FLD, 7kg rated capacity

Lo

ad

siz

e in

kg

Det

ergen

t d

osa

ge

in g

En

erg

y

con

sum

pti

on

in

kW

h

En

erg

y

con

sum

pti

on

in

ma

in w

ash

in

kW

h

Wa

ter

con

sum

pti

on

in L

Wa

ter

con

sum

pti

on

ma

in w

ash

in

L

Wa

ter

con

sum

pti

on

rin

se c

ycl

e i

n L

Du

rati

on

spin

nin

g p

ha

se i

n

min

Du

rati

on

main

wa

sh

in m

in

Wa

shin

g

tem

per

atu

re i

n

°C

Wa

shin

g

Per

form

an

ce

Ind

ex

7,00 62 0,44 0,28 101,1 19,3 81,8 21 45 30 0,84

7,00 62 0,63 0,46 105,2 20,1 85,0 20 44 40 0,84

7,00 62 0,98 0,87 53,3 17,5 35,8 22 87 60 0,99

7,00 124 0,41 0,25 99,5 19,1 80,4 23 45 30 0,85

7,00 124 0,53 0,36 96,9 18,8 78,1 18 45 40 0,90

7,00 124 0,97 0,86 56,6 17,8 38,8 19 87 60 1,04

7,00 186 0,45 0,28 104,0 19,9 84,1 23 45 30 0,89

7,00 186 0,59 0,42 104,1 19,9 84,2 19 44 40 0,92

7,00 186 1,01 0,90 54,5 17,6 36,9 17 88 60 1,12

5,25 51,5 0,39 0,26 68,4 17,3 51,2 19 76 30 0,84

5,25 51,5 0,62 0,49 67,4 16,2 51,2 24 77 40 0,91

5,25 51,5 1,04 0,93 53,0 16,2 36,8 17 87 60 0,96

5,25 103 0,37 0,24 65,4 16,5 49,0 24 75 30 0,92

5,25 103 0,61 0,49 65,9 15,8 50,1 26 77 40 0,98

5,25 103 1,04 0,93 54,4 16,2 38,2 16 87 60 1,04

5,25 154,5 0,41 0,28 66,8 16,6 50,2 21 76 30 0,98

5,25 154,5 0,65 0,52 66,8 16,5 50,3 32 78 40 1,04

5,25 154,5 1,04 0,93 52,9 15,2 37,7 26 86 60 1,11

3,50 41 0,47 0,34 57,9 13,8 44,1 20 75 30 0,91

3,50 41 0,64 0,52 57,7 14,0 43,6 18 77 40 0,96

3,50 41 1,02 0,90 43,1 13,7 29,3 18 85 60 0,99

3,50 82 0,46 0,34 57,9 13,8 44,1 22 76 30 0,99

3,50 82 0,65 0,53 56,9 13,7 43,2 19 77 40 1,01

3,50 82 1,04 0,91 42,9 13,7 29,3 23 84 60 1,08

3,50 123 0,45 0,32 57,1 13,8 43,3 18 75 30 1,02

3,50 123 0,65 0,52 56,4 13,7 42,7 20 77 40 1,06

3,50 123 1,05 0,94 45,4 13,8 31,6 24 84 60 1,14

1,75 30,5 0,51 0,40 56,2 13,6 42,6 26 75 30 0,97

1,75 30,5 0,71 0,59 56,3 13,4 42,9 26 76 40 1,02

1,75 30,5 1,07 0,97 43,0 13,7 29,4 18 83 60 1,01

1,75 61 0,50 0,39 56,6 13,6 43,0 18 74 30 1,05

1,75 61 0,72 0,60 57,1 13,7 43,4 21 76 40 1,06

1,75 61 1,08 0,97 42,5 13,8 28,7 19 83 60 1,12

1,75 91,5 0,50 0,38 56,5 13,7 42,8 24 75 30 1,08

APPENDIX VII

1,75 91,5 0,72 0,60 56,7 13,5 43,2 19 76 40 1,12

1,75 91,5 1,06 0,96 42,4 13,5 28,9 16 83 60 1,17

0,00 0 0,20 0,14 23,2 6,6 16,6 9 28 30

0,00 0 0,29 0,24 22,9 6,6 16,3 9 28 40

0,00 0 0,47 0,42 23,0 6,6 16,4 9 28 60

Table 11-6: BoschLogix8, 8kg rated capacity

Lo

ad

siz

e in

kg

Det

ergen

t

do

sag

e in

g

En

erg

y c

on

sum

pti

on

in

kW

h

En

erg

y c

on

sum

pti

on

in m

ain

wa

sh i

n k

Wh

Wa

ter

con

sum

pti

on

in L

Wa

ter

con

sum

pti

on

ma

in w

ash

in

L

Wa

ter

con

sum

pti

on

rin

se c

ycl

e i

n L

Du

rati

on

sp

inn

ing

ph

ase

in

min

Du

rati

on

main

wa

sh

in m

in

Wa

shin

g

tem

per

atu

re i

n °

C

Wa

shin

g

Per

form

an

ce I

nd

ex

8,00 68 0,26 0,13 78,5 19,8 58,7 11 87 30 0,79

8,00 68 0,76 0,66 79,4 20,8 58,6 11 100 40 0,87

8,00 68 1,00 0,89 78,4 19,8 58,6 12 107 60 0,88

8,00 136 0,30 0,20 78,4 19,8 58,6 12 87 30 0,86

8,00 136 0,78 0,68 79,6 20,9 58,7 12 100 40 0,96

8,00 136 0,99 0,89 78,4 19,8 58,6 12 107 60 0,99

8,00 204 0,29 0,18 78,6 19,8 58,8 11 87 30 0,90

8,00 204 0,77 0,67 79,6 20,9 58,7 11 100 40 0,97

8,00 204 1,15 1,06 78,6 19,9 58,7 12 107 60 1,06

6,00 56 0,35 0,25 78,5 19,9 58,6 12 86 30 0,89

6,00 56 0,82 0,72 79,4 20,9 58,5 11 100 40 0,95

6,00 56 1,24 1,12 78,4 19,8 58,6 11 107 60 0,97

6,00 112 0,32 0,22 78,4 19,8 58,6 11 87 30 0,95

6,00 112 0,81 0,71 79,5 20,9 58,6 12 100 40 1,01

6,00 112 1,22 1,10 78,4 19,8 58,6 16 107 60 1,06

6,00 168 0,32 0,21 78,4 19,8 58,6 12 87 30 0,98

6,00 168 0,81 0,71 79,4 20,9 58,5 12 100 40 1,06

6,00 168 1,23 1,13 78,4 19,8 58,6 11 107 60 1,11

4,00 44 0,26 0,15 58,6 14,1 44,5 12 71 30 0,89

4,00 44 0,62 0,53 58,6 14,1 44,5 11 83 40 0,96

4,00 44 0,89 0,80 58,6 14,1 44,5 12 92 60 1,00

4,00 88 0,22 0,13 58,6 14,1 44,5 11 72 30 0,97

4,00 88 0,63 0,54 58,6 14,1 44,5 11 83 40 1,03

4,00 88 0,92 0,82 58,6 14,1 44,5 13 92 60 1,07

4,00 132 0,24 0,14 58,6 14,1 44,5 12 72 30 0,99

4,00 132 0,60 0,50 58,6 14,1 44,5 13 83 40 1,07

4,00 132 0,92 0,83 58,6 14,1 44,5 14 92 60 1,14

2,00 32 0,29 0,20 49,2 11,5 37,7 12 62 30 0,95

APPENDIX VIII

2,00 32 0,51 0,42 49,2 11,5 37,7 11 73 40 1,00

2,00 32 0,81 0,72 49,2 11,5 37,7 12 82 60 1,04

2,00 64 0,27 0,17 49,1 11,5 37,6 13 62 30 1,00

2,00 64 0,51 0,42 49,2 11,5 37,7 12 72 40 1,06

2,00 64 0,84 0,74 49,2 11,5 37,7 12 82 60 1,11

2,00 96 0,27 0,18 49,2 11,5 37,7 11 62 30 1,03

2,00 96 0,58 0,48 49,2 11,5 37,7 15 72 40 1,10

2,00 96 0,83 0,73 49,2 11,5 37,7 13 82 60 1,15

0,00 0 0,17 0,11 22,9 6,3 16,6 11 42 30

0,00 0 0,34 0,28 22,9 6,4 16,5 11 53 40

0,00 0 0,47 0,41 22,9 6,3 16,6 11 62 60

Table 11-7: Haier HWF1481, 8 kg rated capacity

Load

siz

e in

kg

Det

ergen

t d

osa

ge i

n

g

En

ergy c

on

sum

pti

on

in k

Wh

En

ergy c

on

sum

pti

on

in m

ain

wash

in

kW

h

Wate

r co

nsu

mp

tio

n

in L

Wate

r co

nsu

mp

tio

n

main

wash

in

L

Wate

r co

nsu

mp

tio

n

rin

se c

ycl

e i

n L

Du

rati

on

sp

inn

ing

ph

ase

in

min

Du

rati

on

main

wash

in m

in

Wash

ing

tem

per

atu

re i

n °

C

Wash

ing

Per

form

an

ce I

nd

ex

8,00 68 0,26 0,16 63,2 21,1 42,1 15 61 30 0,77

8,00 68 0,41 0,33 65,5 22,0 43,5 21 61 40 0,82

8,00 68 1,25 1,16 64,2 20,8 43,4 25 73 60 0,90

8,00 136 0,22 0,16 63,5 21,1 42,4 16 61 30 0,88

8,00 136 0,44 0,36 63,0 21,2 41,8 23 61 40 0,91

8,00 136 1,26 1,20 59,9 20,5 60 1,00

8,00 136 1,32 1,25 64,1 21,4 60 1,03

8,00 136 1,26 1,15 59,9 21,0 60 1,00

8,00 204 0,21 0,14 65,4 22,4 43,1 17 61 30 0,89

8,00 204 0,48 0,40 61,2 20,9 40,3 16 61 40 0,96

8,00 204 1,18 1,10 64,3 21,5 42,8 18 72 60 1,04

6,00 56 0,26 0,18 59,1 18,6 40,4 15 61 30 0,83

6,00 56 0,56 0,49 60,1 18,9 41,2 14 61 40 0,88

6,00 56 1,15 1,08 57,3 17,5 39,8 15 72 60 0,94

6,00 112 0,28 0,19 60,3 18,9 41,4 18 61 30 0,91

6,00 112 0,60 0,52 58,7 19,2 39,5 15 61 40 0,95

6,00 112 1,15 1,07 61,2 17,5 43,7 21 72 60 1,01

6,00 168 0,29 0,21 59,3 19,6 39,7 16 61 30 0,94

6,00 168 0,62 0,55 61,4 19,8 41,5 15 61 40 1,00

6,00 168 1,15 1,08 59,1 18,0 41,1 24 72 60 1,09

4,00 44 0,27 0,19 51,9 14,1 37,8 16 61 30 0,87

4,00 44 0,45 0,38 52,0 14,1 37,9 14 61 40 0,91

APPENDIX IX

4,00 44 1,08 1,00 53,2 15,1 38,1 17 72 60 0,98

4,00 88 0,29 0,20 54,1 15,9 38,2 14 61 30 0,95

4,00 88 0,50 0,42 53,4 15,4 38,0 15 61 40 0,99

4,00 88 1,06 0,98 54,1 15,0 39,1 15 72 60 1,06

4,00 132 0,24 0,17 51,9 15,1 36,9 14 61 30 0,99

4,00 132 0,44 0,37 52,2 15,5 36,8 15 61 40 1,01

4,00 132 0,98 0,91 54,7 16,0 38,6 17 72 60 1,11

2,00 32 0,25 0,17 38,4 10,5 27,9 26 61 30 0,92

2,00 32 0,41 0,34 38,4 10,6 27,8 19 61 40 0,96

2,00 32 0,84 0,77 38,2 10,8 27,4 14 73 60 1,02

2,00 64 0,24 0,16 37,7 10,1 27,6 24 61 30 0,98

2,00 64 0,41 0,33 38,2 10,3 27,9 14 61 40 1,01

2,00 64 0,80 0,72 38,1 10,4 27,8 14 72 60 1,09

2,00 96 0,25 0,17 38,0 10,6 27,4 20 62 30 1,06

2,00 96 0,39 0,32 37,1 10,2 27,0 13 61 40 1,12

2,00 96 0,82 0,75 38,2 10,6 27,6 14 72 60 1,18

0,00 0 0,20 0,14 38,6 7,5 31,1 26 61 30

0,00 0 0,32 0,26 38,3 7,5 30,9 16 61 40

0,00 0 0,60 0,54 38,6 7,6 31,0 21 72 60

Table 11-8: INDESIT PWE 8168W, 8kg rated capacity

Load

siz

e in

kg

Det

ergen

t d

osa

ge i

n

g

En

ergy

con

sum

pti

on

in

kW

h

En

ergy

con

sum

pti

on

in

main

wash

in

kW

h

Wate

r

con

sum

pti

on

in

L

Wate

r

con

sum

pti

on

main

wash

in

L

Wate

r

con

sum

pti

on

rin

se

cycl

e i

n L

Du

rati

on

sp

inn

ing

ph

ase

in

min

Du

rati

on

main

wash

in

min

Wash

ing

tem

per

atu

re i

n °

C

Wash

ing

Per

form

an

ce I

nd

ex

8,00 68 0,47 0,35 70,6 23,7 46,9 20 34 30 0,75

8,00 68 1,15 1,03 73,5 25,9 47,6 19 161 40 0,93

8,00 68 1,33 1,21 72,1 25,3 46,8 22 161 60 0,96

8,00 136 0,47 0,34 73,2 24,2 49,0 22 42 30 0,82

8,00 136 1,10 0,98 74,2 25,5 48,7 19 161 40 1,02

8,00 136 1,34 1,23 74,2 27,4 46,8 19 161 60 1,04

8,00 204 0,43 0,32 70,7 24,1 46,6 18 41 30 0,87

8,00 204 1,10 1,00 72,5 26,0 46,5 21 161 40 1,08

8,00 204 1,33 1,21 71,1 24,8 46,3 33 161 60 1,09

6,00 56 0,38 0,29 63,4 20,2 43,2 19 31 30 0,76

6,00 56 1,10 0,97 67,3 23,0 44,3 20 162 40 0,97

6,00 56 1,33 1,23 64,4 21,4 43,0 20 161 60 0,99

6,00 112 0,43 0,32 63,1 21,0 42,1 19 41 30 0,87

6,00 112 1,03 0,93 65,9 22,3 43,5 22 161 40 1,06

6,00 112 1,34 1,24 66,4 22,2 44,3 19 161 60 1,09

APPENDIX X

6,00 168 0,42 0,31 63,6 20,9 42,6 21 41 30 0,91

6,00 168 1,04 0,93 65,6 22,2 43,4 19 161 40 1,10

6,00 168 1,34 1,24 66,6 22,5 44,1 19 161 60 1,14

4,00 44 0,36 0,25 54,2 15,4 38,8 19 26 30 0,79

4,00 44 0,88 0,78 56,6 17,3 39,3 21 160 40 0,97

4,00 44 1,22 1,11 57,6 17,9 39,7 19 155 60 1,01

4,00 88 0,33 0,23 53,9 15,6 38,3 19 26 30 0,85

4,00 88 0,96 0,85 56,5 18,0 38,6 19 160 40 1,05

4,00 88 1,22 1,12 57,0 17,9 39,1 19 156 60 1,10

4,00 132 0,38 0,26 54,8 16,3 38,5 22 26 30 1,09

4,00 132 0,95 0,86 58,2 18,1 40,1 18 161 40 1,14

4,00 132 1,20 1,11 56,6 17,8 38,8 19 156 60 0,88

2,00 32 0,29 0,20 42,2 11,2 31,0 20 22 30 0,81

2,00 32 0,74 0,64 43,1 12,1 31,0 25 159 40 1,04

2,00 32 0,93 0,84 42,8 12,8 30,1 32 152 60 1,07

2,00 64 0,28 0,19 41,2 11,5 29,7 19 23 30 0,90

2,00 64 0,76 0,67 41,8 12,4 29,4 19 159 40 1,10

2,00 64 0,94 0,84 41,2 12,2 29,0 27 153 60 1,15

2,00 96 0,30 0,21 41,3 11,2 30,1 19 23 30 0,95

2,00 96 0,70 0,60 41,4 11,7 29,8 19 159 40 1,15

2,00 96 0,93 0,84 42,9 12,4 30,4 20 147 60 1,19

0,00 0 0,19 0,11 30,9 6,2 24,7 22 21 30

0,00 0 0,45 0,38 32,1 7,0 25,1 18 158 40

0,00 0 0,57 0,49 31,6 6,8 24,8 18 147 60

Table 11-9: Bauknecht WAB-1210, 11kg rated capacity

Lo

ad

siz

e in

kg

Det

ergen

t d

osa

ge i

n

g

En

ergy

con

sum

pti

on

in

kW

h

En

ergy

con

sum

pti

on

in

ma

in w

ash

in

kW

h

Wa

ter

con

sum

pti

on

in L

Wa

ter

con

sum

pti

on

ma

in w

ash

in

L

Wa

ter

con

sum

pti

on

rin

se c

ycl

e i

n L

Du

rati

on

sp

inn

ing

ph

ase

in

min

Du

rati

on

main

wash

in m

in

Wa

shin

g

tem

per

atu

re i

n °

C

Wa

shin

g

Per

form

an

ce I

nd

ex

11,00 86 0,74 0,59 82,8 25,5 56,7 9 169 30 0,90

11,00 172 0,77 0,56 82,4 25,5 56,3 9 169 30 1,02

11,00 258 0,71 0,56 82,7 25,5 56,3 9 169 30 1,04

8,25 69,5 1,00 0,78 84,0 26,5 57,3 9 167 30 0,97

8,25 139 0,97 0,78 83,2 25,6 54,8 8 167 30 1,04

8,25 208,5 0,94 0,78 83,2 25,5 56,8 8 167 30 1,07

5,50 53 0,67 0,55 55,0 16,8 37,4 8 173 30 0,97

5,50 106 0,63 0,50 55,0 17,0 37,5 8 173 30 1,04

APPENDIX XI

5,50 159 0,68 0,54 55,0 16,4 36,9 8 173 30 1,08

2,75 36,5 0,50 0,40 42,1 12,3 28,3 8 124 30 0,96

2,75 73 0,56 0,46 45,8 13,6 30,9 8 124 30 1,07

2,75 109,5 0,51 0,41 42,7 12,7 28,8 8 124 30 1,13

11,00 86 0,99 0,84 82,9 25,5 56,2 9 173 40 0,96

11,00 172 1,04 0,85 82,4 25,5 56,3 9 173 40 1,03

11,00 258 1,04 0,85 83,3 26,0 56,9 9 173 40 1,08

8,25 69,5 1,35 1,16 83,4 26,1 57,3 8 175 40 0,99

8,25 139 1,31 1,15 82,7 25,7 57,0 8 174 40 1,09

8,25 208,5 1,32 1,16 82,7 25,5 57,2 8 174 40 1,11

5,50 53 0,81 0,68 53,8 16,2 36,4 8 173 40 0,99

5,50 106 0,82 0,69 55,0 16,4 37,0 8 173 40 1,08

5,50 159 0,81 0,69 54,7 16,4 37,2 8 173 40 1,11

2,75 36,5 0,66 0,56 44,6 13,1 44,6 8 124 40 0,97

2,75 73 0,68 0,58 45,4 13,5 30,6 8 124 40 1,08

2,75 109,5 0,66 0,55 44,5 13,0 29,8 8 124 40 1,14

11,00 86 1,44 1,25 82,8 25,5 57,3 8 186 60 0,95

11,00 172 1,33 1,17 82,9 25,5 57,1 9 187 60 1,05

11,00 172 1,38 1,20 82,7 25,5 57,1 9 188 60 1,05

11,00 172 1,38 1,21 82,6 25,5 57,1 9 188 60 1,03

11,00 258 1,35 1,19 82,7 25,4 57,2 9 186 60 1,09

8,25 69,5 1,78 1,53 84,0 26,8 57,9 9 190 60 1,00

8,25 139 1,66 1,50 83,6 26,1 57,5 8 189 60 1,07

8,25 208,5 1,68 1,50 83,3 25,5 57,9 8 189 60 1,14

5,50 86 1,15 1,01 55,7 17,4 38,0 60 0,99

5,50 106 1,12 0,97 54,7 16,4 37,0 8 181 60 1,07

5,50 159 1,04 0,91 53,5 15,3 35,6 8 181 60 1,14

2,75 36,5 1,02 0,91 45,2 13,3 30,6 8 125 60 1,01

2,75 73 1,01 0,92 45,8 13,6 30,9 8 125 60 1,12

2,75 109,5 1,03 0,92 47,4 13,9 31,4 8 125 60 1,18

0,00 1,12 0,97 54,0 15,4 36,4 8 181 60 0,00

12.2 Virtual washing machine MATLAB source code

function [ DET, CWmw, CEmw, vektorWASCHKORB] =

Waschmaschine(BETA, WP, TEMP, WMV, MWD, WASCHKORB, m,

WochenCounterGENERAL, RESTLOAD, REG1ODERNOT2, PERSON,

LOADamoountTOTAL,WOCHENLA, n, SZX)

if TEMP ==30;

WP = .93;

end

if TEMP ==40;

APPENDIX XII

WP = 1;

end

if TEMP ==60;

WP = 1.04;

end

if WMV ==5;

if TEMP ==30;

MWD = 48;

end

if TEMP ==40;

MWD = 69;

end

if TEMP ==60;

MWD = 68;

end

end

if WMV ==6;

if TEMP ==30;

MWD = 50;

end

if TEMP ==40;

MWD = 69;

end

if TEMP ==60;

MWD = 68;

end

end

if WMV ==7;

if TEMP ==30;

MWD = 65;

end

if TEMP ==40;

MWD = 69;

end

if TEMP ==60;

MWD = 82;

end

end

if WMV ==8;

if TEMP ==30;

MWD = 68;

end

if TEMP ==40;

MWD = 102; % Dauer des hauptwaschganges

end

if TEMP ==60;

MWD = 108; % Dauer des hauptwaschganges in minuten in einer

kg waschmaschine

APPENDIX XIII

end

end

if WMV ==9;

if TEMP ==30;

MWD = 120;

end

if TEMP ==40;

MWD = 130;

end

if TEMP ==60;

MWD = 140;

end

end

if WMV ==10;

if TEMP ==30;

MWD = 140;

end

if TEMP ==40;

MWD = 158;

end

if TEMP ==60;

MWD = 173;

end

end

if WMV ==11;

if TEMP ==30;

MWD = 156;

end

if TEMP ==40;

MWD = 158;

end

if TEMP ==60;

MWD = 173;

end

end

DET=0;

CWmw =0;

CEmw =0;

DET = ((WP - BETA.const.DET)/BETA.det.DET) -

((BETA.temp.DET*TEMP)/BETA.det.DET) -

((BETA.load.DET*WASCHKORB)/BETA.det.DET) -

((BETA.mwd.DET*MWD)/BETA.det.DET);

if DET<40

DET = 40;

end

CWmw = BETA.const.WATER + (BETA.load.WATER *WASCHKORB)+

(BETA.wmv.WATER *WMV);

APPENDIX XIV

CEmw = BETA.const.ENERGY + (BETA.temp.ENERGY * TEMP) +

(BETA.water.ENERGY * CWmw) + (BETA.mwd.ENERGY * MWD);

if REG1ODERNOT2 == 2;

RESTLOAD =0;

end

vektorWASCHKORB (1,:) = [n,m,

WochenCounterGENERAL,PERSON,LOADamoountTOTAL,WOCHENLA,

WASCHKORB, TEMP,WMV,MWD, RESTLOAD,WP, DET, CWmw,CEmw,

REG1ODERNOT2, SZX];

end

12.3 Virtual washing household MATLAB source code

CO2TOTAL=0;

PERSON =0;

myArray = zeros(7,12);

WMV=5;

MAXIMALEWARTEZEITWASCHEN = 0;

SZX=0;

for WMV=5:11;

for PERSON = 1:7;

WochenLA = 4;

Amountof30 = .23;

Amountof40 = .46;

Amountof60 = .31;

LB30 = .5;

LB40 = .7;

LB60 = .9;

MWD30 = 52;

MWD40 = 77;

MWD60 = 84;

BETA.const.DET = 0.8204235;

BETA.temp.DET = 0.002605017;

BETA.load.DET =-0.034011332;

BETA.wmv.DET =-0.000505483;

BETA.mwd.DET =0.001346586;

BETA.det.DET=0.001163081;

BETA.const.WATER =3.949488643;

BETA.load.WATER =1.703739258;

BETA.wmv.WATER =.513236840;

BETA.const.ENERGY =-.85617463;

BETA.temp.ENERGY =.02019412;

BETA.water.ENERGY =.02422291;

BETA.mwd.ENERGY =.00250646;

WP = 1.03;

Weekcounter = 0;

DOS = 52;

APPENDIX XV

co2water = 0.59;

co2energy = 600;

co2detergent = 2;

LOADamoountTOTAL = PERSON * WochenLA;

WMRECOMENDEDload30 = WMV*LB30;



WOCHENLA30 = round(LOADamoountTOTAL * Amountof30);



WochenCounterGENERAL = 0

m=0;

WASCHKORB= 0;

WASCHKORB30 = 0;

RESTLOAD =0;

TEMP=30;

MWD = MWD30;

WochenCounterREG30 = 0;

WochenCounterNOT30 = 0;

WieoftgewaschenREG30 = 0;

WieoftgewaschenNOT30 = 0;

NOTWASCHGANG = 0;

REG1ODERNOT2 = 0;

DETwaschwoche =0;

CWmwwaschwoche =0;

CEmwwaschwoche =0;

CO2DETwaschwoche = 0;

CO2CWmwwaschwoche =0;

CO2CEmwwaschwoche =0;

CEmwwaschwocheSCHLEIFE=0;

DETwaschwocheSCHLEIFE =0;

CWmwwaschwocheSCHLEIFE = 0;

vektorWASCHKORB1 = zeros (1,17);

for n=1:(DOS);

REG1ODERNOT2 = 3;

WochenCounterGENERAL = WochenCounterGENERAL+1;

Waschkorb30 = WOCHENLA30 + RESTLOAD;

if Waschkorb30 < WMRECOMENDEDload30 && WochenCounterNOT30 <

MAXIMALEWARTEZEITWASCHEN

RESTLOAD = Waschkorb30;

WochenCounterNOT30 = WochenCounterNOT30 +1;

elseif Waschkorb30 < WMRECOMENDEDload30 &&

WochenCounterNOT30 == MAXIMALEWARTEZEITWASCHEN

m=m+1;

WASCHKORB = Waschkorb30;

REG1ODERNOT2 = 2;

WOCHENLA = WOCHENLA30;

APPENDIX XVI

[DET, CWmw, CEmw, vektorWASCHKORB] = Waschmaschine(BETA,

WP, TEMP, WMV, MWD, WASCHKORB, m, WochenCounterGENERAL,

RESTLOAD, REG1ODERNOT2, PERSON, LOADamoountTOTAL,

WOCHENLA,n,SZX);

vektorWASCHKORB = vertcat(vektorWASCHKORB1,

vektorWASCHKORB);

vektorWASCHKORB1=vektorWASCHKORB;

WochenCounterNOT30 = 0;

NOTWASCHGANG = NOTWASCHGANG+1; % Anzahl der

NOTWaschgägnge

RESTLOAD =0;

REG1ODERNOT2 = 0;

end

while Waschkorb30 >= WMRECOMENDEDload30 %Reguläres Waschen

m=m+1;

RESTLOAD = Waschkorb30 - WMRECOMENDEDload30;

WASCHKORB = WMRECOMENDEDload30;

REG1ODERNOT2 = 1;

WOCHENLA = WOCHENLA30;

[DET,CWmw,CEmw, vektorWASCHKORB] = Waschmaschine(BETA,

WP, TEMP, WMV, MWD, WASCHKORB, m, WochenCounterGENERAL,

RESTLOAD, REG1ODERNOT2, PERSON, LOADamoountTOTAL, WOCHENLA,n,

SZX);

vektorWASCHKORB = vertcat(vektorWASCHKORB1,

vektorWASCHKORB); %

vektorWASCHKORB1=vektorWASCHKORB;

WochenCounterREG30 = WochenCounterREG30 +1;

Waschkorb30=RESTLOAD;

WochenCounterNOT30 =0;

REG1ODERNOT2 = 0;

end

vNOTWASCHGANG(n+1,1) =NOTWASCHGANG;

vWochenCounterGENERAL (n+1,1) = WochenCounterGENERAL;

if REG1ODERNOT2 == 3;

DET = 0;

CWmw =0;

CEmw =0;

REG1ODERNOT2=3;

WASCHKORB=0;

WOCHENLA =0;

vektorWASCHKORB=zeros(1,17); %

vektorWASCHKORB (1,:) = [n, m, WochenCounterGENERAL,

PERSON, LOADamoountTOTAL, WOCHENLA, WASCHKORB, TEMP,WMV,MWD,

RESTLOAD, WP, DET, CWmw, CEmw, REG1ODERNOT2, SZX];

vektorWASCHKORB2 = vertcat(vektorWASCHKORB1,

vektorWASCHKORB);

vektorWASCHKORB1=vektorWASCHKORB2;

end

APPENDIX XVII

end

TEST30 = vektorWASCHKORB1;

vektorWASCHKORB1=zeros(1,17);

ACKNOWLEDGEMENTS

Acknowledgements

First of all I would like to express my thanks to Prof. Dr. Rainer Stamminger who gave

me this exciting topic. His interest in my work, qualified support, swift feedback and

many helpful discussions as well as the technical and editorial advices were an

important prerequisite for the successful completion of this thesis.

I also would like to thank Prof. Dr. Michael-Burkhard Piorkowsky who agreed to

become the second examiner.

Furthermore, I would like to thank to Henkel KGaA who made this work possible.

Especially, I want to thank to Dr. Christian Nitsch and Mr. Arnd Kessler for their

support and the readiness for discussions.

I also would like to thank the team of the Section of Appliance Technology at

University of Bonn. Especially I would like to thank Marina Niestrath and Stefan Dodel

who conducted the washing machines tests. Furthermore I would like to thank to Dr.

Gereon Broil for the technical assistance and to Maria Braun, Edith Lambert, Ina Hook

and Yvonne Drechsler for their help.

I would like to thank all my friends, especially to Maria Braun, Rico Baumgartl and Jan

Henning Eggert. You always believed in me and made sure that I never felt alone.

Last but not least I will forever be thankful to my family, especially my parents Mirsada

and Esad as well as my brother Admir for their never-ending support in my life.

ACKNOWLEDGEMENTS

CURRICULUM VITAE

Curriculum vitae

Emir Lasic

Date of birth: December 4th

, 1979

Sanski Most, Bosnia and Herzegovina

July 1998 Matura in Sarajevo, Bosnia and Herzegovina

2002 - 2005 Study of food technology science, University of Bonn

2006 - 2009 Study of nutrition and home economics science, University of Bonn

2009 Diploma Thesis: ”Akzeptanz von Haushaltsrobotern am Beispiel von

Care-o-bot 3”

2009 -2014 Ph.D. student at “Household and Appliance Technology Section” of the

“Institute of Agricultural Engineering” of the University of Bonn

Sustainable use of washing machine: modeling the...

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